{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}31 Mar 2020, 16:53:56

{com}. use "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta"

. set more off

. gen gender = Q2

. recode gender 2 = 0 .=0
{txt}(gender: 715 changes made)

{com}. sum gender

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}      1,348    .4695846    .4992593          0          1

{com}. gen income = Q3

. tabulate income

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        250       18.55       18.55
{txt}          2 {c |}{res}        340       25.22       43.77
{txt}          3 {c |}{res}        153       11.35       55.12
{txt}          4 {c |}{res}        371       27.52       82.64
{txt}          5 {c |}{res}        234       17.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. rename income education
{res}
{com}. tab education

  {txt}education {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        250       18.55       18.55
{txt}          2 {c |}{res}        340       25.22       43.77
{txt}          3 {c |}{res}        153       11.35       55.12
{txt}          4 {c |}{res}        371       27.52       82.64
{txt}          5 {c |}{res}        234       17.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen income = Q4

. tab income

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        309       22.92       22.92
{txt}          2 {c |}{res}        212       15.73       38.65
{txt}          3 {c |}{res}        187       13.87       52.52
{txt}          4 {c |}{res}        156       11.57       64.09
{txt}          5 {c |}{res}        121        8.98       73.07
{txt}          6 {c |}{res}        100        7.42       80.49
{txt}          7 {c |}{res}        263       19.51      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen age = Q5

. tab age

        {txt}age {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}        176       13.06       13.06
{txt}          3 {c |}{res}        306       22.70       35.76
{txt}          4 {c |}{res}        323       23.96       59.72
{txt}          5 {c |}{res}        196       14.54       74.26
{txt}          6 {c |}{res}        188       13.95       88.20
{txt}          7 {c |}{res}        143       10.61       98.81
{txt}          8 {c |}{res}         16        1.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen ethnic = Q6

. tab ethnic

     {txt}ethnic {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,060       78.64       78.64
{txt}          2 {c |}{res}        166       12.31       90.95
{txt}          3 {c |}{res}         18        1.34       92.28
{txt}          4 {c |}{res}         69        5.12       97.40
{txt}          5 {c |}{res}          1        0.07       97.48
{txt}          6 {c |}{res}         34        2.52      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen white = ethnic

. recode white 2=0 3= 0 4=0 5=0 6=0 .=0
{txt}(white: 288 changes made)

{com}. sum white

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}white {c |}{res}      1,348    .7863501    .4100345          0          1

{com}. gen vote = Q7

. tab vote

       {txt}vote {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        471       34.94       34.94
{txt}          2 {c |}{res}        487       36.13       71.07
{txt}          3 {c |}{res}         91        6.75       77.82
{txt}          4 {c |}{res}        299       22.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen party = Q8

. tab party

      {txt}party {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        537       39.84       39.84
{txt}          2 {c |}{res}        435       32.27       72.11
{txt}          3 {c |}{res}        255       18.92       91.02
{txt}          4 {c |}{res}         22        1.63       92.66
{txt}          5 {c |}{res}         75        5.56       98.22
{txt}          6 {c |}{res}         24        1.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen dem = party

. gen gop=party

. recode dem 2=0 3=0 4=0 5=0 6=0
{txt}(dem: 811 changes made)

{com}. sum dem

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}dem {c |}{res}      1,348     .398368    .4897437          0          1

{com}. recode gop 1=0 2=1 3=0 4=0 5=0 6=0 .=0
{txt}(gop: 1348 changes made)

{com}. sum gop

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}gop {c |}{res}      1,348    .3227003    .4676827          0          1

{com}. gen shelter = Q9

. tab shelter

    {txt}shelter {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,066       79.08       79.08
{txt}          2 {c |}{res}        282       20.92      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. gen jobloss = Q10

. tab jobloss

    {txt}jobloss {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        411       30.49       30.49
{txt}          2 {c |}{res}        937       69.51      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. recode shelter 2=0
{txt}(shelter: 282 changes made)

{com}. recode jobloss 2=0
{txt}(jobloss: 937 changes made)

{com}. sum shelter jobloss

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}shelter {c |}{res}      1,348    .7908012    .4068876          0          1
{txt}{space 5}jobloss {c |}{res}      1,348    .3048961    .4605343          0          1

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. sum Q11_1- Q11_6

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}Q11_1 {c |}{res}      1,348    3.330119    .8138086          1          4
{txt}{space 7}Q11_2 {c |}{res}      1,348     3.14911    .8268997          1          4
{txt}{space 7}Q11_3 {c |}{res}      1,348    2.998516    .8761447          1          4
{txt}{space 7}Q11_4 {c |}{res}      1,348    2.485905    .8924047          1          4
{txt}{space 7}Q11_5 {c |}{res}      1,348    2.077893    .9130015          1          4
{txt}{hline 13}{c +}{hline 57}
{space 7}Q11_6 {c |}{res}      1,348    2.921365     .974685          1          4

{com}. gen gov_ideology = Q11_1+ Q11_2+ Q11_3

. sum gov_ideology

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
gov_ideology {c |}{res}      1,348    9.477745    2.221634          3         12

{com}. gen ideology_rs = ( gov_ideology - r(3) ) / ( r(12)-r(3) ) * 100
{err}3 invalid name
{txt}{search r(198), local:r(198);}

{com}. gen ideology_rs = ( gov_ideology - r(min) ) / ( r(max)-r(min) ) * 100

. sum ideology_rs

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}ideology_rs {c |}{res}      1,348    71.97494    24.68483          0        100

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. gen cdc_econ = Q13
{txt}(1,214 missing values generated)

{com}. gen cdc_health = Q15
{txt}(1,224 missing values generated)

{com}. gen pres_health = Q16
{txt}(1,214 missing values generated)

{com}. gen pres_econ = Q17
{txt}(1,197 missing values generated)

{com}. gen state_econ = Q18
{txt}(1,209 missing values generated)

{com}. gen state_health = Q19
{txt}(1,229 missing values generated)

{com}. gen expert_health = Q20
{txt}(1,220 missing values generated)

{com}. gen expert_econ = Q21
{txt}(1,201 missing values generated)

{com}. gen control_econ = Q22
{txt}(1,200 missing values generated)

{com}. gen control_health = Q23
{txt}(1,226 missing values generated)

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. sum cdc_econ- control_health

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}cdc_econ {c |}{res}        134    1.731343    .4449242          1          2
{txt}{space 2}cdc_health {c |}{res}        124    1.830645    .3765866          1          2
{txt}{space 1}pres_health {c |}{res}        134    1.828358    .3784837          1          2
{txt}{space 3}pres_econ {c |}{res}        151    1.642384    .4808932          1          2
{txt}{space 2}state_econ {c |}{res}        139    1.697842    .4608542          1          2
{txt}{hline 13}{c +}{hline 57}
state_health {c |}{res}        119    1.789916    .4090905          1          2
{txt}expert_hea~h {c |}{res}        128    1.726563    .4474749          1          2
{txt}{space 1}expert_econ {c |}{res}        147    1.721088    .4499972          1          2
{txt}control_econ {c |}{res}        148    1.668919    .4722001          1          2
{txt}control_he~h {c |}{res}        122    1.819672    .3860457          1          2

{com}. gen distance = Q26
{txt}(2 missing values generated)

{com}. sum distance

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}distance {c |}{res}      1,346    1.537147    .8770696          1          5

{com}. recode distance 5=1 4=2 3=3 2=4 1=5
{txt}(distance: 1263 changes made)

{com}. tab distance

   {txt}distance {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         24        1.78        1.78
{txt}          2 {c |}{res}         41        3.05        4.83
{txt}          3 {c |}{res}         83        6.17       11.00
{txt}          4 {c |}{res}        338       25.11       36.11
{txt}          5 {c |}{res}        860       63.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,346      100.00

{com}. hist distance
{txt}(bin={res}31{txt}, start={res}1{txt}, width={res}.12903226{txt})
{res}
{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. gen trust = Q27
{txt}(2 missing values generated)

{com}. recode trust 5=1 4=2 3=3 2=4 1=5
{txt}(trust: 1155 changes made)

{com}. gen knowledge = Q28
{txt}(2 missing values generated)

{com}. recode knowledge 5=1 4=2 3=3 2=4 1=5
{txt}(knowledge: 1162 changes made)

{com}. gen attention = Q25
{txt}(2 missing values generated)

{com}. tab attention

  {txt}attention {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,044       77.56       77.56
{txt}          3 {c |}{res}         91        6.76       84.32
{txt}          4 {c |}{res}        211       15.68      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,346      100.00

{com}. recode attention 3=0 4=0
{txt}(attention: 302 changes made)

{com}. sum attention

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}attention {c |}{res}      1,346    .7756315    .4173208          0          1

{com}. gen manipulate = Q24
{txt}(2 missing values generated)

{com}. recode manipulate 5=1 4=2 3=3 2=4 1=5
{txt}(manipulate: 1213 changes made)

{com}. sum manipulate

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}manipulate {c |}{res}      1,346    4.336553    .7627682          1          5

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. ologit distance gender education income age white gop shelter jobloss ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1278.3289}  
Iteration 2:{space 3}log likelihood = {res:-1277.8772}  
Iteration 3:{space 3}log likelihood = {res:-1277.8771}  
Iteration 4:{space 3}log likelihood = {res:-1277.8771}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     90.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1277.8771{txt}{col 49}Pseudo R2{col 67}= {res}    0.0344

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2}-.6183528{col 26}{space 2} .1165376{col 37}{space 1}   -5.31{col 46}{space 3}0.000{col 54}{space 4}-.8467623{col 67}{space 3}-.3899433
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0349711{col 26}{space 2} .0456444{col 37}{space 1}    0.77{col 46}{space 3}0.444{col 54}{space 4}-.0544902{col 67}{space 3} .1244324
{txt}{space 6}income {c |}{col 14}{res}{space 2} .1433537{col 26}{space 2} .0301194{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .0843209{col 67}{space 3} .2023866
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0347706{col 26}{space 2} .0374669{col 37}{space 1}    0.93{col 46}{space 3}0.353{col 54}{space 4}-.0386632{col 67}{space 3} .1082044
{txt}{space 7}white {c |}{col 14}{res}{space 2} .3931516{col 26}{space 2} .1411101{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .1165808{col 67}{space 3} .6697223
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.1645973{col 26}{space 2} .1249447{col 37}{space 1}   -1.32{col 46}{space 3}0.188{col 54}{space 4}-.4094844{col 67}{space 3} .0802898
{txt}{space 5}shelter {c |}{col 14}{res}{space 2}  .031175{col 26}{space 2} .1382048{col 37}{space 1}    0.23{col 46}{space 3}0.822{col 54}{space 4}-.2397014{col 67}{space 3} .3020513
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2870101{col 26}{space 2} .1278068{col 37}{space 1}    2.25{col 46}{space 3}0.025{col 54}{space 4} .0365133{col 67}{space 3} .5375069
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2}  .010925{col 26}{space 2} .0023309{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .0063564{col 67}{space 3} .0154935
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.506624{col 26}{space 2}  .347444{col 54}{space 4}-3.187601{col 67}{space 3}-1.825646
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.472008{col 26}{space 2} .3074896{col 54}{space 4}-2.074676{col 67}{space 3}-.8693394
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} -.561651{col 26}{space 2} .2946431{col 54}{space 4}-1.139141{col 67}{space 3} .0158388
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.036917{col 26}{space 2} .2936311{col 54}{space 4} .4614103{col 67}{space 3} 1.612423
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum manipulate

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}manipulate {c |}{res}      1,346    4.336553    .7627682          1          5

{com}. sum attention

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}attention {c |}{res}      1,346    .7756315    .4173208          0          1

{com}. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res}31 Mar 2020, 19:20:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 1 Apr 2020, 09:02:20

{com}. gen econ_frame = cdc_econ+ pres_econ+ state_econ+ expert_econ+ control_econ
{txt}(1,348 missing values generated)

{com}. recode econ_frame .=1 if cdc_econ ==1
{txt}(econ_frame: 36 changes made)

{com}. recode econ_frame .=1 if cdc_econ ==2
{txt}(econ_frame: 98 changes made)

{com}. recode econ_frame .=1 if pres_econ ==2
{txt}(econ_frame: 97 changes made)

{com}. recode econ_frame .=1 if pres_econ ==1
{txt}(econ_frame: 54 changes made)

{com}. recode econ_frame .=1 if state_econ ==1
{txt}(econ_frame: 42 changes made)

{com}. recode econ_frame .=1 if state_econ ==2
{txt}(econ_frame: 97 changes made)

{com}. recode econ_frame .=1 if expert_econ ==2
{txt}(econ_frame: 106 changes made)

{com}. recode econ_frame .=1 if expert_econ ==1
{txt}(econ_frame: 41 changes made)

{com}. recode econ_frame .=1 if control_econ ==1
{txt}(econ_frame: 49 changes made)

{com}. recode econ_frame .=1 if control_econ ==2
{txt}(econ_frame: 99 changes made)

{com}. sum econ_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}econ_frame {c |}{res}        719           1           0          1          1

{com}. recode econ_frame .=0
{txt}(econ_frame: 629 changes made)

{com}. sum econ_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}econ_frame {c |}{res}      1,348    .5333828    .4990695          0          1

{com}. gen health_frame = .
{txt}(1,348 missing values generated)

{com}. recode health_frame .=1 if cdc_health ==1
{txt}(health_frame: 21 changes made)

{com}. recode health_frame .=1 if cdc_health ==2
{txt}(health_frame: 103 changes made)

{com}. recode health_frame .=1 if pres_health ==1
{txt}(health_frame: 23 changes made)

{com}. recode health_frame .=1 if pres_health ==2
{txt}(health_frame: 111 changes made)

{com}. recode health_frame .=1 if state_health ==2
{txt}(health_frame: 94 changes made)

{com}. recode health_frame .=1 if state_health ==1
{txt}(health_frame: 25 changes made)

{com}. recode health_frame .=1 if expert_health ==1
{txt}(health_frame: 35 changes made)

{com}. recode health_frame .=1 if expert_health ==2
{txt}(health_frame: 93 changes made)

{com}. recode health_frame .=1 if control_health ==2
{txt}(health_frame: 100 changes made)

{com}. recode health_frame .=1 if control_health ==1
{txt}(health_frame: 22 changes made)

{com}. sum health_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
health_frame {c |}{res}        627           1           0          1          1

{com}. recode health_frame .=0
{txt}(health_frame: 721 changes made)

{com}. sum health_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
health_frame {c |}{res}      1,348    .4651335     .498968          0          1

{com}. gen shop = .
{txt}(1,348 missing values generated)

{com}. rename shop no_shop
{res}
{com}. recode no_shop .=1 if cdc_econ ==2
{txt}(no_shop: 98 changes made)

{com}. recode no_shop .=1 if cdc_health ==2
{txt}(no_shop: 103 changes made)

{com}. recode no_shop .=1 if pres_health ==2
{txt}(no_shop: 111 changes made)

{com}. recode no_shop .=1 if pres_econ ==2
{txt}(no_shop: 97 changes made)

{com}. recode no_shop .=1 if state_econ ==2
{txt}(no_shop: 97 changes made)

{com}. recode no_shop .=1 if state_health ==2
{txt}(no_shop: 94 changes made)

{com}. recode no_shop .=1 if expert_health ==2
{txt}(no_shop: 93 changes made)

{com}. recode no_shop .=1 if expert_econ ==2
{txt}(no_shop: 106 changes made)

{com}. recode no_shop .=1 if control_econ ==2
{txt}(no_shop: 99 changes made)

{com}. recode no_shop .=1 if control_health ==2
{txt}(no_shop: 100 changes made)

{com}. sum no_shop

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}        998           1           0          1          1

{com}. recode no_shop .=0
{txt}(no_shop: 350 changes made)

{com}. sum no_shop

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}      1,348    .7403561    .4386019          0          1

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. gen cdc_m = .
{txt}(1,348 missing values generated)

{com}. recode cdc_m .=0 if cdc_econ == .
{txt}(cdc_m: 1214 changes made)

{com}. recode cdc_m .=0 if cdc_health == .
{txt}(cdc_m: 134 changes made)

{com}. sum cdc_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}cdc_m {c |}{res}      1,348           0           0          0          0

{com}. recode cdc_m 0=1 if cdc_econ == 1
{txt}(cdc_m: 36 changes made)

{com}. recode cdc_m 0=1 if cdc_econ == 2
{txt}(cdc_m: 98 changes made)

{com}. sum cdc_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}cdc_m {c |}{res}      1,348    .0994065    .2993181          0          1

{com}. gen pres_m = .
{txt}(1,348 missing values generated)

{com}. recode cdc_m 0=1 if cdc_health == 1
{txt}(cdc_m: 21 changes made)

{com}. recode cdc_m 0=1 if cdc_health == 2
{txt}(cdc_m: 103 changes made)

{com}. sum cdc_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}cdc_m {c |}{res}      1,348    .1913947     .393545          0          1

{com}. recode pres_m .=1 if pres_health ==1
{txt}(pres_m: 23 changes made)

{com}. recode pres_m .=1 if pres_health ==2
{txt}(pres_m: 111 changes made)

{com}. recode pres_m .=1 if pres_econ ==2
{txt}(pres_m: 97 changes made)

{com}. recode pres_m .=1 if pres_econ ==1
{txt}(pres_m: 54 changes made)

{com}. sum pres_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}pres_m {c |}{res}        285           1           0          1          1

{com}. recode pres_m .=0
{txt}(pres_m: 1063 changes made)

{com}. sum pres_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}pres_m {c |}{res}      1,348    .2114243    .4084701          0          1

{com}. gen state_m = .
{txt}(1,348 missing values generated)

{com}. recode state_m .=1 if state_econ ==1
{txt}(state_m: 42 changes made)

{com}. recode state_m .=1 if state_econ ==2
{txt}(state_m: 97 changes made)

{com}. recode state_m .=1 if state_health ==2
{txt}(state_m: 94 changes made)

{com}. recode state_m .=1 if state_health ==1
{txt}(state_m: 25 changes made)

{com}. sum state_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}state_m {c |}{res}        258           1           0          1          1

{com}. recode state_m .=0
{txt}(state_m: 1090 changes made)

{com}. sum state_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}state_m {c |}{res}      1,348    .1913947     .393545          0          1

{com}. gen expert_m = .
{txt}(1,348 missing values generated)

{com}. recode expert_m .=1 if expert_health ==1
{txt}(expert_m: 35 changes made)

{com}. recode expert_m .=1 if expert_health ==2
{txt}(expert_m: 93 changes made)

{com}. recode expert_m .=1 if expert_econ ==2
{txt}(expert_m: 106 changes made)

{com}. recode expert_m .=1 if expert_econ ==1
{txt}(expert_m: 41 changes made)

{com}. sum expert_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}expert_m {c |}{res}        275           1           0          1          1

{com}. recode expert_m .=0
{txt}(expert_m: 1073 changes made)

{com}. sum expert_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}expert_m {c |}{res}      1,348    .2040059    .4031229          0          1

{com}. gen control_m = .
{txt}(1,348 missing values generated)

{com}. recode control_m .=1 if control_econ ==1
{txt}(control_m: 49 changes made)

{com}. recode control_m .=1 if control_econ ==2
{txt}(control_m: 99 changes made)

{com}. recode control_m .=1 if control_health ==2
{txt}(control_m: 100 changes made)

{com}. recode control_m .=1 if control_health ==1
{txt}(control_m: 22 changes made)

{com}. sum control_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}control_m {c |}{res}        270           1           0          1          1

{com}. recode control_m .=0
{txt}(control_m: 1078 changes made)

{com}. sum control_m

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}control_m {c |}{res}      1,348    .2002967    .4003709          0          1

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. logit no_shop cdc_m pres_m state_m expert_m econ_frame health_frame ideology_rs jobloss shelter gop white age income education gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-735.07551}  
Iteration 2:{space 3}log likelihood = {res:-734.45486}  
Iteration 3:{space 3}log likelihood = {res:-734.42267}  
Iteration 4:{space 3}log likelihood = {res:-734.41493}  
Iteration 5:{space 3}log likelihood = {res:-734.41342}  
Iteration 6:{space 3}log likelihood = {res:-734.41318}  
Iteration 7:{space 3}log likelihood = {res:-734.41312}  
Iteration 8:{space 3}log likelihood = {res:-734.41311}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     75.13
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-734.41311{txt}{col 49}Pseudo R2{col 67}= {res}    0.0487

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .1212384{col 26}{space 2} .2103437{col 37}{space 1}    0.58{col 46}{space 3}0.564{col 54}{space 4}-.2910277{col 67}{space 3} .5335046
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2} -.130813{col 26}{space 2} .1986767{col 37}{space 1}   -0.66{col 46}{space 3}0.510{col 54}{space 4}-.5202121{col 67}{space 3} .2585862
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.0858932{col 26}{space 2} .2051255{col 37}{space 1}   -0.42{col 46}{space 3}0.675{col 54}{space 4}-.4879319{col 67}{space 3} .3161455
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.1825559{col 26}{space 2} .2001754{col 37}{space 1}   -0.91{col 46}{space 3}0.362{col 54}{space 4}-.5748924{col 67}{space 3} .2097806
{txt}{space 2}econ_frame {c |}{col 14}{res}{space 2} 13.91334{col 26}{space 2} 614.9014{col 37}{space 1}    0.02{col 46}{space 3}0.982{col 54}{space 4}-1191.271{col 67}{space 3} 1219.098
{txt}health_frame {c |}{col 14}{res}{space 2} 14.49429{col 26}{space 2} 614.9014{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4} -1190.69{col 67}{space 3} 1219.679
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0078353{col 26}{space 2} .0025906{col 37}{space 1}    3.02{col 46}{space 3}0.002{col 54}{space 4} .0027578{col 67}{space 3} .0129127
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .0274438{col 26}{space 2} .1420114{col 37}{space 1}    0.19{col 46}{space 3}0.847{col 54}{space 4}-.2508935{col 67}{space 3} .3057811
{txt}{space 5}shelter {c |}{col 14}{res}{space 2}  .046464{col 26}{space 2}  .157553{col 37}{space 1}    0.29{col 46}{space 3}0.768{col 54}{space 4}-.2623342{col 67}{space 3} .3552621
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0602307{col 26}{space 2} .1417714{col 37}{space 1}   -0.42{col 46}{space 3}0.671{col 54}{space 4}-.3380974{col 67}{space 3} .2176361
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4684258{col 26}{space 2}  .158577{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .1576204{col 67}{space 3} .7792311
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0332066{col 26}{space 2} .0422683{col 37}{space 1}   -0.79{col 46}{space 3}0.432{col 54}{space 4}-.1160509{col 67}{space 3} .0496378
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0409588{col 26}{space 2} .0333884{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.0244812{col 67}{space 3} .1063988
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0358963{col 26}{space 2} .0520228{col 37}{space 1}    0.69{col 46}{space 3}0.490{col 54}{space 4}-.0660665{col 67}{space 3} .1378591
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6801964{col 26}{space 2} .1313819{col 37}{space 1}   -5.18{col 46}{space 3}0.000{col 54}{space 4}-.9377001{col 67}{space 3}-.4226927
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-13.76023{col 26}{space 2} 614.9015{col 37}{space 1}   -0.02{col 46}{space 3}0.982{col 54}{space 4}-1218.945{col 67}{space 3} 1191.425
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. gen cdc_ideol = cdc_m* ideology_rs

. gen pres_ideol = ideology_rs* pres_m

. gen state_ideol = state_m* ideology_rs

. gen expert_ideol = ideology_rs* expert_m

. logit no_shop cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol econ_frame health_frame ideology_rs jobloss shelter gop white age income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-733.13636}  
Iteration 2:{space 3}log likelihood = {res:-732.46116}  
Iteration 3:{space 3}log likelihood = {res:-732.42251}  
Iteration 4:{space 3}log likelihood = {res:-732.41966}  
Iteration 5:{space 3}log likelihood = {res:-732.41903}  
Iteration 6:{space 3}log likelihood = {res:-732.41893}  
Iteration 7:{space 3}log likelihood = {res:-732.41891}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}18{txt}){col 67}= {res}     79.12
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.41891{txt}{col 49}Pseudo R2{col 67}= {res}    0.0512

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .1359839{col 26}{space 2} .6549163{col 37}{space 1}    0.21{col 46}{space 3}0.836{col 54}{space 4}-1.147628{col 67}{space 3} 1.419596
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.9724012{col 26}{space 2} .5666724{col 37}{space 1}   -1.72{col 46}{space 3}0.086{col 54}{space 4}-2.083059{col 67}{space 3} .1382564
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.9003236{col 26}{space 2} .5863305{col 37}{space 1}   -1.54{col 46}{space 3}0.125{col 54}{space 4} -2.04951{col 67}{space 3} .2488631
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.8079846{col 26}{space 2} .5790058{col 37}{space 1}   -1.40{col 46}{space 3}0.163{col 54}{space 4}-1.942815{col 67}{space 3}  .326846
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0002712{col 26}{space 2} .0086004{col 37}{space 1}    0.03{col 46}{space 3}0.975{col 54}{space 4}-.0165853{col 67}{space 3} .0171277
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0121388{col 26}{space 2} .0076086{col 37}{space 1}    1.60{col 46}{space 3}0.111{col 54}{space 4}-.0027737{col 67}{space 3} .0270513
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0118608{col 26}{space 2} .0080034{col 37}{space 1}    1.48{col 46}{space 3}0.138{col 54}{space 4}-.0038255{col 67}{space 3} .0275472
{txt}expert_ideol {c |}{col 14}{res}{space 2} .0090787{col 26}{space 2} .0078366{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4}-.0062808{col 67}{space 3} .0244382
{txt}{space 2}econ_frame {c |}{col 14}{res}{space 2} 14.10102{col 26}{space 2} 699.9408{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1357.758{col 67}{space 3}  1385.96
{txt}health_frame {c |}{col 14}{res}{space 2}  14.6841{col 26}{space 2} 699.9408{col 37}{space 1}    0.02{col 46}{space 3}0.983{col 54}{space 4}-1357.175{col 67}{space 3} 1386.543
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0009693{col 26}{space 2} .0054205{col 37}{space 1}    0.18{col 46}{space 3}0.858{col 54}{space 4}-.0096547{col 67}{space 3} .0115933
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .0359138{col 26}{space 2} .1426795{col 37}{space 1}    0.25{col 46}{space 3}0.801{col 54}{space 4} -.243733{col 67}{space 3} .3155605
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0630277{col 26}{space 2}  .157951{col 37}{space 1}    0.40{col 46}{space 3}0.690{col 54}{space 4}-.2465505{col 67}{space 3}  .372606
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0674619{col 26}{space 2} .1421382{col 37}{space 1}   -0.47{col 46}{space 3}0.635{col 54}{space 4}-.3460476{col 67}{space 3} .2111238
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4742196{col 26}{space 2} .1586261{col 37}{space 1}    2.99{col 46}{space 3}0.003{col 54}{space 4} .1633181{col 67}{space 3}  .785121
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0292126{col 26}{space 2} .0421847{col 37}{space 1}   -0.69{col 46}{space 3}0.489{col 54}{space 4} -.111893{col 67}{space 3} .0534679
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0535141{col 26}{space 2} .0300888{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0054589{col 67}{space 3} .1124871
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6809285{col 26}{space 2} .1313221{col 37}{space 1}   -5.19{col 46}{space 3}0.000{col 54}{space 4} -.938315{col 67}{space 3} -.423542
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-13.45178{col 26}{space 2}  699.941{col 37}{space 1}   -0.02{col 46}{space 3}0.985{col 54}{space 4}-1385.311{col 67}{space 3} 1358.407
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol econ_frame health_frame ideology_rs jobloss shelter gop white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-733.36119}  
Iteration 2:{space 3}log likelihood = {res:-732.70055}  
Iteration 3:{space 3}log likelihood = {res:-732.66171}  
Iteration 4:{space 3}log likelihood = {res:-732.65897}  
Iteration 5:{space 3}log likelihood = {res:-732.65839}  
Iteration 6:{space 3}log likelihood = {res:-732.65826}  
Iteration 7:{space 3}log likelihood = {res:-732.65823}  
Iteration 8:{space 3}log likelihood = {res:-732.65822}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}17{txt}){col 67}= {res}     78.64
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.65822{txt}{col 49}Pseudo R2{col 67}= {res}    0.0509

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .1456048{col 26}{space 2} .6550988{col 37}{space 1}    0.22{col 46}{space 3}0.824{col 54}{space 4}-1.138365{col 67}{space 3} 1.429575
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.9731989{col 26}{space 2} .5668363{col 37}{space 1}   -1.72{col 46}{space 3}0.086{col 54}{space 4}-2.084178{col 67}{space 3} .1377797
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.8930048{col 26}{space 2} .5861609{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-2.041859{col 67}{space 3} .2558495
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.8032588{col 26}{space 2}  .579171{col 37}{space 1}   -1.39{col 46}{space 3}0.165{col 54}{space 4}-1.938413{col 67}{space 3} .3318954
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0001999{col 26}{space 2} .0086038{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.0166632{col 67}{space 3} .0170631
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0122244{col 26}{space 2} .0076088{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0026885{col 67}{space 3} .0271373
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0117852{col 26}{space 2} .0079999{col 37}{space 1}    1.47{col 46}{space 3}0.141{col 54}{space 4}-.0038944{col 67}{space 3} .0274647
{txt}expert_ideol {c |}{col 14}{res}{space 2} .0091009{col 26}{space 2} .0078373{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}  -.00626{col 67}{space 3} .0244618
{txt}{space 2}econ_frame {c |}{col 14}{res}{space 2} 14.54317{col 26}{space 2} 843.2234{col 37}{space 1}    0.02{col 46}{space 3}0.986{col 54}{space 4}-1638.144{col 67}{space 3} 1667.231
{txt}health_frame {c |}{col 14}{res}{space 2} 15.12745{col 26}{space 2} 843.2234{col 37}{space 1}    0.02{col 46}{space 3}0.986{col 54}{space 4} -1637.56{col 67}{space 3} 1667.815
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0008561{col 26}{space 2} .0054194{col 37}{space 1}    0.16{col 46}{space 3}0.874{col 54}{space 4}-.0097657{col 67}{space 3} .0114779
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2}  .051416{col 26}{space 2} .1408545{col 37}{space 1}    0.37{col 46}{space 3}0.715{col 54}{space 4}-.2246538{col 67}{space 3} .3274859
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0656891{col 26}{space 2} .1578811{col 37}{space 1}    0.42{col 46}{space 3}0.677{col 54}{space 4}-.2437521{col 67}{space 3} .3751303
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0710996{col 26}{space 2} .1420415{col 37}{space 1}   -0.50{col 46}{space 3}0.617{col 54}{space 4}-.3494959{col 67}{space 3} .2072968
{txt}{space 7}white {c |}{col 14}{res}{space 2}  .452462{col 26}{space 2} .1553914{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .1479003{col 67}{space 3} .7570236
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0531598{col 26}{space 2} .0300668{col 37}{space 1}    1.77{col 46}{space 3}0.077{col 54}{space 4}  -.00577{col 67}{space 3} .1120896
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6803849{col 26}{space 2} .1313114{col 37}{space 1}   -5.18{col 46}{space 3}0.000{col 54}{space 4}-.9377505{col 67}{space 3}-.4230194
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-14.00385{col 26}{space 2} 843.2236{col 37}{space 1}   -0.02{col 46}{space 3}0.987{col 54}{space 4}-1666.692{col 67}{space 3} 1638.684
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol econ_frame health_frame ideology_rs jobloss shelter white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-733.48407}  
Iteration 2:{space 3}log likelihood = {res:-732.82542}  
Iteration 3:{space 3}log likelihood = {res:-732.78663}  
Iteration 4:{space 3}log likelihood = {res:-732.78389}  
Iteration 5:{space 3}log likelihood = {res:-732.78332}  
Iteration 6:{space 3}log likelihood = {res:-732.78319}  
Iteration 7:{space 3}log likelihood = {res:-732.78316}  
Iteration 8:{space 3}log likelihood = {res:-732.78315}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     78.39
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.78315{txt}{col 49}Pseudo R2{col 67}= {res}    0.0508

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .141308{col 26}{space 2} .6551731{col 37}{space 1}    0.22{col 46}{space 3}0.829{col 54}{space 4}-1.142808{col 67}{space 3} 1.425424
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2} -.971678{col 26}{space 2} .5667276{col 37}{space 1}   -1.71{col 46}{space 3}0.086{col 54}{space 4}-2.082444{col 67}{space 3} .1390877
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.8888461{col 26}{space 2} .5861499{col 37}{space 1}   -1.52{col 46}{space 3}0.129{col 54}{space 4}-2.037679{col 67}{space 3} .2599866
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.8071076{col 26}{space 2} .5789384{col 37}{space 1}   -1.39{col 46}{space 3}0.163{col 54}{space 4}-1.941806{col 67}{space 3} .3275908
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0002937{col 26}{space 2} .0086038{col 37}{space 1}    0.03{col 46}{space 3}0.973{col 54}{space 4}-.0165695{col 67}{space 3} .0171569
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0122411{col 26}{space 2} .0076084{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0026711{col 67}{space 3} .0271532
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0117397{col 26}{space 2} .0080009{col 37}{space 1}    1.47{col 46}{space 3}0.142{col 54}{space 4}-.0039417{col 67}{space 3} .0274211
{txt}expert_ideol {c |}{col 14}{res}{space 2} .0091762{col 26}{space 2} .0078338{col 37}{space 1}    1.17{col 46}{space 3}0.241{col 54}{space 4}-.0061777{col 67}{space 3} .0245301
{txt}{space 2}econ_frame {c |}{col 14}{res}{space 2}  14.5906{col 26}{space 2} 843.6776{col 37}{space 1}    0.02{col 46}{space 3}0.986{col 54}{space 4}-1638.987{col 67}{space 3} 1668.168
{txt}health_frame {c |}{col 14}{res}{space 2} 15.17938{col 26}{space 2} 843.6776{col 37}{space 1}    0.02{col 46}{space 3}0.986{col 54}{space 4}-1638.398{col 67}{space 3} 1668.757
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2}  .000891{col 26}{space 2} .0054173{col 37}{space 1}    0.16{col 46}{space 3}0.869{col 54}{space 4}-.0097267{col 67}{space 3} .0115087
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .0540347{col 26}{space 2} .1407361{col 37}{space 1}    0.38{col 46}{space 3}0.701{col 54}{space 4} -.221803{col 67}{space 3} .3298723
{txt}{space 5}shelter {c |}{col 14}{res}{space 2}  .065917{col 26}{space 2} .1578483{col 37}{space 1}    0.42{col 46}{space 3}0.676{col 54}{space 4}  -.24346{col 67}{space 3} .3752941
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4350753{col 26}{space 2} .1513413{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1384517{col 67}{space 3} .7316988
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0521485{col 26}{space 2} .0299949{col 37}{space 1}    1.74{col 46}{space 3}0.082{col 54}{space 4}-.0066405{col 67}{space 3} .1109375
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6854504{col 26}{space 2} .1309331{col 37}{space 1}   -5.24{col 46}{space 3}0.000{col 54}{space 4}-.9420745{col 67}{space 3}-.4288264
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-14.06162{col 26}{space 2} 843.6777{col 37}{space 1}   -0.02{col 46}{space 3}0.987{col 54}{space 4} -1667.64{col 67}{space 3} 1639.516
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol econ_frame health_frame ideology_rs jobloss shelter white income gender

{txt}note: health_frame omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1274.9763}  
Iteration 2:{space 3}log likelihood = {res:-1274.3546}  
Iteration 3:{space 3}log likelihood = {res:-1274.3543}  
Iteration 4:{space 3}log likelihood = {res:-1274.3543}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     97.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1274.3543{txt}{col 49}Pseudo R2{col 67}= {res}    0.0370

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1203119{col 26}{space 2} .5638577{col 37}{space 1}   -0.21{col 46}{space 3}0.831{col 54}{space 4}-1.225453{col 67}{space 3} .9848288
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.4200787{col 26}{space 2} .5106281{col 37}{space 1}   -0.82{col 46}{space 3}0.411{col 54}{space 4}-1.420891{col 67}{space 3}  .580734
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-1.040985{col 26}{space 2}  .521631{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-2.063363{col 67}{space 3}-.0186075
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} .3472789{col 26}{space 2} .5293378{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-.6902041{col 67}{space 3} 1.384762
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0011323{col 26}{space 2} .0075251{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4}-.0136167{col 67}{space 3} .0158813
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0036785{col 26}{space 2} .0068773{col 37}{space 1}    0.53{col 46}{space 3}0.593{col 54}{space 4}-.0098008{col 67}{space 3} .0171577
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0127081{col 26}{space 2} .0072199{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0014426{col 67}{space 3} .0268589
{txt}expert_ideol {c |}{col 14}{res}{space 2}-.0061059{col 26}{space 2} .0071477{col 37}{space 1}   -0.85{col 46}{space 3}0.393{col 54}{space 4}-.0201151{col 67}{space 3} .0079034
{txt}{space 2}econ_frame {c |}{col 14}{res}{space 2}-.1528225{col 26}{space 2} .1146177{col 37}{space 1}   -1.33{col 46}{space 3}0.182{col 54}{space 4} -.377469{col 67}{space 3}  .071824
{txt}health_frame {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0089626{col 26}{space 2} .0049579{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0007547{col 67}{space 3} .0186799
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2811577{col 26}{space 2} .1266502{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0329279{col 67}{space 3} .5293874
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0517353{col 26}{space 2} .1390726{col 37}{space 1}    0.37{col 46}{space 3}0.710{col 54}{space 4} -.220842{col 67}{space 3} .3243125
{txt}{space 7}white {c |}{col 14}{res}{space 2}  .368838{col 26}{space 2} .1359001{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1024787{col 67}{space 3} .6351974
{txt}{space 6}income {c |}{col 14}{res}{space 2} .1520717{col 26}{space 2} .0275859{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .0980043{col 67}{space 3} .2061391
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6308188{col 26}{space 2} .1164686{col 37}{space 1}   -5.42{col 46}{space 3}0.000{col 54}{space 4}-.8590931{col 67}{space 3}-.4025445
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -3.00933{col 26}{space 2}  .442823{col 54}{space 4}-3.877247{col 67}{space 3}-2.141413
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.973456{col 26}{space 2} .4122942{col 54}{space 4}-2.781538{col 67}{space 3}-1.165374
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-1.059106{col 26}{space 2}   .40293{col 54}{space 4}-1.848835{col 67}{space 3}-.2693781
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} .5482067{col 26}{space 2} .4008275{col 54}{space 4}-.2374007{col 67}{space 3} 1.333814
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1274.9763}  
Iteration 2:{space 3}log likelihood = {res:-1274.3546}  
Iteration 3:{space 3}log likelihood = {res:-1274.3543}  
Iteration 4:{space 3}log likelihood = {res:-1274.3543}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     97.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1274.3543{txt}{col 49}Pseudo R2{col 67}= {res}    0.0370

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1203119{col 26}{space 2} .5638577{col 37}{space 1}   -0.21{col 46}{space 3}0.831{col 54}{space 4}-1.225453{col 67}{space 3} .9848288
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.4200787{col 26}{space 2} .5106281{col 37}{space 1}   -0.82{col 46}{space 3}0.411{col 54}{space 4}-1.420891{col 67}{space 3}  .580734
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-1.040985{col 26}{space 2}  .521631{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-2.063363{col 67}{space 3}-.0186075
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} .3472789{col 26}{space 2} .5293378{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-.6902041{col 67}{space 3} 1.384762
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0011323{col 26}{space 2} .0075251{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4}-.0136167{col 67}{space 3} .0158813
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0036785{col 26}{space 2} .0068773{col 37}{space 1}    0.53{col 46}{space 3}0.593{col 54}{space 4}-.0098008{col 67}{space 3} .0171577
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0127081{col 26}{space 2} .0072199{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0014426{col 67}{space 3} .0268589
{txt}expert_ideol {c |}{col 14}{res}{space 2}-.0061059{col 26}{space 2} .0071477{col 37}{space 1}   -0.85{col 46}{space 3}0.393{col 54}{space 4}-.0201151{col 67}{space 3} .0079034
{txt}health_frame {c |}{col 14}{res}{space 2} .1528225{col 26}{space 2} .1146177{col 37}{space 1}    1.33{col 46}{space 3}0.182{col 54}{space 4} -.071824{col 67}{space 3}  .377469
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0089626{col 26}{space 2} .0049579{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0007547{col 67}{space 3} .0186799
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2811577{col 26}{space 2} .1266502{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0329279{col 67}{space 3} .5293874
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0517353{col 26}{space 2} .1390726{col 37}{space 1}    0.37{col 46}{space 3}0.710{col 54}{space 4} -.220842{col 67}{space 3} .3243125
{txt}{space 7}white {c |}{col 14}{res}{space 2}  .368838{col 26}{space 2} .1359001{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1024787{col 67}{space 3} .6351974
{txt}{space 6}income {c |}{col 14}{res}{space 2} .1520717{col 26}{space 2} .0275859{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .0980043{col 67}{space 3} .2061391
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6308188{col 26}{space 2} .1164686{col 37}{space 1}   -5.42{col 46}{space 3}0.000{col 54}{space 4}-.8590931{col 67}{space 3}-.4025445
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.856508{col 26}{space 2} .4399299{col 54}{space 4}-3.718755{col 67}{space 3}-1.994261
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.820634{col 26}{space 2} .4091697{col 54}{space 4}-2.622591{col 67}{space 3}-1.018676
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.9062839{col 26}{space 2} .3999561{col 54}{space 4}-1.690183{col 67}{space 3}-.1223844
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} .7010292{col 26}{space 2} .3985304{col 54}{space 4} -.080076{col 67}{space 3} 1.482134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. set more off

. set seed 32306

. bayesmh distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender, likelihood(oprobit) prior({c -(}distance: cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender{c )-}, normal(0,100)) prior({c -(}distance:_cut1 _cut2 _cut3 _cut4{c )-}, exponential({c -(}lambda=4{c )-})) prior({c -(}lambda{c )-}, uniform(0,10)) block({c -(}distance: cdc_m{c )-}) block({c -(}distance: pres_m{c )-}) block({c -(}distance: state_m{c )-}) block({c -(}distance: expert_m{c )-}) block({c -(}distance: cdc_ideol{c )-}) block({c -(}distance: pres_ideol{c )-}) block({c -(}distance: state_ideol{c )-}) block({c -(}distance: expert_ideol{c )-}) block({c -(}distance: health_frame{c )-}) block({c -(}distance: ideology_rs{c )-}) block({c -(}distance: jobloss{c )-}) block({c -(}distance: shelter{c )-}) block({c -(}distance: white{c )-}) block({c -(}distance: income{c )-}) block({c -(}distance: gender{c )-}) block({c -(}distance:_cut1 _cut2 _cut3 _cut4{c )-}) block(lambda) search(repeat(20000)) dots burnin(100000) mcmcsize(500000) saving(distance_simdata1)
{res}  
{txt}Burn-in {res}100000 {txt}note: invalid initial state
{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}1000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}2000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}3000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}4000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}5000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}6000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}7000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}8000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}9000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}10000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}11000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}12000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}13000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}14000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}15000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}16000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}17000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}18000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}19000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}20000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}21000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}22000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}23000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}24000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}25000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}26000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}27000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}28000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}29000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}30000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}31000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}32000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}33000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}34000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}35000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}36000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}37000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}38000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}39000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}40000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}41000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}42000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}43000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}44000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}45000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}46000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}47000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}48000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}49000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}50000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}51000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}52000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}53000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}54000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}55000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}56000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}57000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}58000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}59000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}60000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}61000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}62000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}63000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}64000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}65000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}66000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}67000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}68000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}69000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}70000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}71000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}72000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}73000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}74000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}75000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}76000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}77000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}78000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}79000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}80000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}81000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}82000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}83000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}84000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}85000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}86000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}87000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}88000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}89000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}90000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}91000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}92000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}93000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}94000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}95000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}96000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}97000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}98000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}99000{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}{bf:a}100000 done
Simulation {res}500000 {txt}.........1000.........2000.........3000.........4000.........5000.........6000.........7000.........8000.........9000.........10000.........11000.........12000.........13000.........14000.........15000.........16000.........17000.........18000.........19000.........20000.........21000.........22000.........23000.........24000.........25000.........26000.........27000.........28000.........29000.........30000.........31000.........32000.........33000.........34000.........35000.........36000.........37000.........38000.........39000.........40000.........41000.........42000.........43000.........44000.........45000.........46000.........47000.........48000.........49000.........50000.........51000.........52000.........53000.........54000.........55000.........56000.........57000.........58000.........59000.........60000.........61000.........62000.........63000.........64000.........65000.........66000.........67000.........68000.........69000.........70000.........71000.........72000.........73000.........74000.........75000.........76000.........77000.........78000.........79000.........80000.........81000.........82000.........83000.........84000.........85000.........86000.........87000.........88000.........89000.........90000.........91000.........92000.........93000.........94000.........95000.........96000.........97000.........98000.........99000.........100000.........101000.........102000.........103000.........104000.........105000.........106000.........107000.........108000.........109000.........110000.........111000.........112000.........113000.........114000.........115000.........116000.........117000.........118000.........119000.........120000.........121000.........122000.........123000.........124000.........125000.........126000.........127000.........128000.........129000.........130000.........131000.........132000.........133000.........134000.........135000.........136000.........137000.........138000.........139000.........140000.........141000.........142000.........143000.........144000.........145000.........146000.........147000.........148000.........149000.........150000.........151000.........152000.........153000.........154000.........155000.........156000.........157000.........158000.........159000.........160000.........161000.........162000.........163000.........164000.........165000.........166000.........167000.........168000.........169000.........170000.........171000.........172000.........173000.........174000.........175000.........176000.........177000.........178000.........179000.........180000.........181000.........182000.........183000.........184000.........185000.........186000.........187000.........188000.........189000.........190000.........191000.........192000.........193000.........194000.........195000.........196000.........197000.........198000.........199000.........200000.........201000.........202000.........203000.........204000.........205000.........206000.........207000.........208000.........209000.........210000.........211000.........212000.........213000.........214000.........215000.........216000.........217000.........218000.........219000.........220000.........221000.........222000.........223000.........224000.........225000.........226000.........227000.........228000.........229000.........230000.........231000.........232000.........233000.........234000.........235000.........236000.........237000.........238000.........239000.........240000.........241000.........242000.........243000.........244000.........245000.........246000.........247000.........248000.........249000.........250000.........251000.........252000.........253000.........254000.........255000.........256000.........257000.........258000.........259000.........260000.........261000.........262000.........263000.........264000.........265000.........266000.........267000.........268000.........269000.........270000.........271000.........272000.........273000.........274000.........275000.........276000.........277000.........278000.........279000.........280000.........281000.........282000.........283000.........284000.........285000.........286000.........287000.........288000.........289000.........290000.........291000.........292000.........293000.........294000.........295000.........296000.........297000.........298000.........299000.........300000.........301000.........302000.........303000.........304000.........305000.........306000.........307000.........308000.........309000.........310000.........311000.........312000.........313000.........314000.........315000.........316000.........317000.........318000.........319000.........320000.........321000.........322000.........323000.........324000.........325000.........326000.........327000.........328000.........329000.........330000.........331000.........332000.........333000.........334000.........335000.........336000.........337000.........338000.........339000.........340000.........341000.........342000.........343000.........344000.........345000.........346000.........347000.........348000.........349000.........350000.........351000.........352000.........353000.........354000.........355000.........356000.........357000.........358000.........359000.........360000.........361000.........362000.........363000.........364000.........365000.........366000.........367000.........368000.........369000.........370000.........371000.........372000.........373000.........374000.........375000.........376000.........377000.........378000.........379000.........380000.........381000.........382000.........383000.........384000.........385000.........386000.........387000.........388000.........389000.........390000.........391000.........392000.........393000.........394000.........395000.........396000.........397000.........398000.........399000.........400000.........401000.........402000.........403000.........404000.........405000.........406000.........407000.........408000.........409000.........410000.........411000.........412000.........413000.........414000.........415000.........416000.........417000.........418000.........419000.........420000.........421000.........422000.........423000.........424000.........425000.........426000.........427000.........428000.........429000.........430000.........431000.........432000.........433000.........434000.........435000.........436000.........437000.........438000.........439000.........440000.........441000.........442000.........443000.........444000.........445000.........446000.........447000.........448000.........449000.........450000.........451000.........452000.........453000.........454000.........455000.........456000.........457000.........458000.........459000.........460000.........461000.........462000.........463000.........464000.........465000.........466000.........467000.........468000.........469000.........470000.........471000.........472000.........473000.........474000.........475000.........476000.........477000.........478000.........479000.........480000.........481000.........482000.........483000.........484000.........485000.........486000.........487000.........488000.........489000.........490000.........491000.........492000.........493000.........494000.........495000.........496000.........497000.........498000.........499000.........500000 done
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ oprobit(xb_distance,{res}{c -(}distance:_cut1 ... _cut4{c )-}{txt}){p_end}

Priors: 
{p 0 31}{space 12}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 11}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 10}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 9}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 8}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 7}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 6}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 5}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 5}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 6}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 10}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 10}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 12}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 11}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 11}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,100){space 31}(1){p_end}
{p 0 31}{space 2}{res}{c -(}distance:_cut1 ... _cut4{c )-}{txt} ~ exponential({res}{c -(}lambda{c )-}{txt}){p_end}

Hyperprior: 
{p 0 13}{space 2}{res}{c -(}lambda{c )-}{txt} ~ uniform(0,10){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered probit regression{col 50}MCMC iterations{col 67}={col 69}{res}   600,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}   100,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   500,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .4314
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .006346
{col 63}{txt}avg ={col 69}{res}    .02644
{txt}Log marginal-likelihood = {res}-1402.1504{col 63}{txt}max ={col 69}{res}     .1003
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1} 1.065862{col 25}{space 2} .2786141{col 36}{space 2} .004536{col 46}{space 2} 1.064353{col 57}{space 2} .5235778{col 68}{space 2} 1.612551
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1} .8461376{col 25}{space 2} .2398829{col 36}{space 2} .003695{col 46}{space 2} .8441134{col 57}{space 2} .3787869{col 68}{space 2} 1.319467
{txt}{space 5}state_m {c |}{col 14}{res}{space 1} .5347989{col 25}{space 2} .2480478{col 36}{space 2} .003715{col 46}{space 2} .5345792{col 57}{space 2} .0528496{col 68}{space 2} 1.021563
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} 1.280266{col 25}{space 2} .2603226{col 36}{space 2} .004158{col 46}{space 2}  1.27914{col 57}{space 2} .7756907{col 68}{space 2} 1.795733
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1}-.0140962{col 25}{space 2} .0037705{col 36}{space 2} .000064{col 46}{space 2}-.0140581{col 57}{space 2}  -.02153{col 68}{space 2}-.0067433
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} -.012045{col 25}{space 2} .0032928{col 36}{space 2} .000053{col 46}{space 2}-.0120259{col 57}{space 2}-.0185139{col 68}{space 2}-.0056176
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1}-.0073788{col 25}{space 2} .0035058{col 36}{space 2} .000054{col 46}{space 2}-.0073837{col 57}{space 2} -.014266{col 68}{space 2}-.0005453
{txt}expert_ideol {c |}{col 14}{res}{space 1} -.017297{col 25}{space 2} .0035758{col 36}{space 2} .000059{col 46}{space 2}-.0172722{col 57}{space 2}-.0244199{col 68}{space 2}-.0103437
{txt}health_frame {c |}{col 14}{res}{space 1} .1223505{col 25}{space 2} .0663129{col 36}{space 2} .000322{col 46}{space 2} .1223565{col 57}{space 2}-.0074623{col 68}{space 2} .2522124
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0191813{col 25}{space 2} .0017368{col 36}{space 2} .000031{col 46}{space 2} .0191805{col 57}{space 2} .0157892{col 68}{space 2} .0226073
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .2250645{col 25}{space 2} .0735456{col 36}{space 2} .000328{col 46}{space 2} .2250593{col 57}{space 2} .0806683{col 68}{space 2} .3690806
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .1010058{col 25}{space 2} .0790076{col 36}{space 2} .000655{col 46}{space 2} .1013561{col 57}{space 2}-.0542714{col 68}{space 2} .2546343
{txt}{space 7}white {c |}{col 14}{res}{space 1} .3325188{col 25}{space 2} .0763569{col 36}{space 2}  .00064{col 46}{space 2} .3324686{col 57}{space 2} .1819807{col 68}{space 2} .4819155
{txt}{space 6}income {c |}{col 14}{res}{space 1}   .09867{col 25}{space 2} .0156893{col 36}{space 2} .000114{col 46}{space 2} .0986828{col 57}{space 2} .0679092{col 68}{space 2} .1294332
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.2871891{col 25}{space 2} .0672288{col 36}{space 2} .000319{col 46}{space 2}-.2871505{col 57}{space 2}-.4189571{col 68}{space 2}-.1550566
{txt}{space 7}_cut1 {c |}{col 14}{res}{space 1} .0314151{col 25}{space 2} .0304642{col 36}{space 2} .000465{col 46}{space 2} .0220615{col 57}{space 2} .0008094{col 68}{space 2} .1140759
{txt}{space 7}_cut2 {c |}{col 14}{res}{space 1} .3909331{col 25}{space 2} .0605346{col 36}{space 2}  .00088{col 46}{space 2} .3872325{col 57}{space 2} .2827731{col 68}{space 2} .5209414
{txt}{space 7}_cut3 {c |}{col 14}{res}{space 1} .8087739{col 25}{space 2} .0709032{col 36}{space 2} .001175{col 46}{space 2} .8060149{col 57}{space 2} .6781834{col 68}{space 2} .9562383
{txt}{space 7}_cut4 {c |}{col 14}{res}{space 1} 1.712615{col 25}{space 2} .0786267{col 36}{space 2} .001161{col 46}{space 2} 1.710561{col 57}{space 2} 1.564499{col 68}{space 2}  1.87246
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 6}lambda {c |}{col 14}{res}{space 1} 1.427104{col 25}{space 2} 1.109699{col 36}{space 2} .006699{col 46}{space 2} 1.099606{col 57}{space 2} .4011031{col 68}{space 2} 4.545333
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{txt}file distance_simdata1.dta saved
{res}
{com}. oprobit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1277.9085}  
Iteration 2:{space 3}log likelihood = {res:-1277.8008}  
Iteration 3:{space 3}log likelihood = {res:-1277.8008}  
{res}
{txt}Ordered probit regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     91.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1277.8008{txt}{col 49}Pseudo R2{col 67}= {res}    0.0344

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .0529646{col 26}{space 2} .3261689{col 37}{space 1}    0.16{col 46}{space 3}0.871{col 54}{space 4}-.5863147{col 67}{space 3}  .692244
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1583096{col 26}{space 2} .2944014{col 37}{space 1}   -0.54{col 46}{space 3}0.591{col 54}{space 4}-.7353259{col 67}{space 3} .4187066
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.4743727{col 26}{space 2} .2991391{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-1.060675{col 67}{space 3} .1119292
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} .3131738{col 26}{space 2} .3074336{col 37}{space 1}    1.02{col 46}{space 3}0.308{col 54}{space 4} -.289385{col 67}{space 3} .9157325
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2}-.0009629{col 26}{space 2} .0043414{col 37}{space 1}   -0.22{col 46}{space 3}0.824{col 54}{space 4}-.0094718{col 67}{space 3} .0075461
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0009855{col 26}{space 2} .0039559{col 37}{space 1}    0.25{col 46}{space 3}0.803{col 54}{space 4}-.0067679{col 67}{space 3} .0087388
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0057306{col 26}{space 2} .0041018{col 37}{space 1}    1.40{col 46}{space 3}0.162{col 54}{space 4}-.0023087{col 67}{space 3} .0137699
{txt}expert_ideol {c |}{col 14}{res}{space 2}-.0046889{col 26}{space 2} .0041428{col 37}{space 1}   -1.13{col 46}{space 3}0.258{col 54}{space 4}-.0128086{col 67}{space 3} .0034308
{txt}health_frame {c |}{col 14}{res}{space 2} .0718696{col 26}{space 2} .0668044{col 37}{space 1}    1.08{col 46}{space 3}0.282{col 54}{space 4}-.0590646{col 67}{space 3} .2028038
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2}  .005912{col 26}{space 2} .0027867{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0004501{col 67}{space 3} .0113739
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .1708905{col 26}{space 2}  .073712{col 37}{space 1}    2.32{col 46}{space 3}0.020{col 54}{space 4} .0264176{col 67}{space 3} .3153635
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0037808{col 26}{space 2} .0816413{col 37}{space 1}    0.05{col 46}{space 3}0.963{col 54}{space 4}-.1562333{col 67}{space 3} .1637949
{txt}{space 7}white {c |}{col 14}{res}{space 2}  .204953{col 26}{space 2} .0799052{col 37}{space 1}    2.56{col 46}{space 3}0.010{col 54}{space 4} .0483416{col 67}{space 3} .3615644
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0872813{col 26}{space 2} .0157503{col 37}{space 1}    5.54{col 46}{space 3}0.000{col 54}{space 4} .0564112{col 67}{space 3} .1181514
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.3484347{col 26}{space 2} .0676392{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-.4810051{col 67}{space 3}-.2158642
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -1.39413{col 26}{space 2} .2361648{col 54}{space 4}-1.857004{col 67}{space 3}-.9312552
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.9499304{col 26}{space 2} .2294865{col 54}{space 4}-1.399716{col 67}{space 3}-.5001451
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.4991599{col 26}{space 2} .2273936{col 54}{space 4}-.9448431{col 67}{space 3}-.0534767
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  .419518{col 26}{space 2} .2275675{col 54}{space 4} -.026506{col 67}{space 3}  .865542
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m1

. m1
{err}command {bf}m1{sf} is unrecognized
{txt}{search r(199), local:r(199);}

{com}. estimates drop m1
{res}
{com}. erase distance_simdata1.dta

. ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1274.9763}  
Iteration 2:{space 3}log likelihood = {res:-1274.3546}  
Iteration 3:{space 3}log likelihood = {res:-1274.3543}  
Iteration 4:{space 3}log likelihood = {res:-1274.3543}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     97.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1274.3543{txt}{col 49}Pseudo R2{col 67}= {res}    0.0370

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1203119{col 26}{space 2} .5638577{col 37}{space 1}   -0.21{col 46}{space 3}0.831{col 54}{space 4}-1.225453{col 67}{space 3} .9848288
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.4200787{col 26}{space 2} .5106281{col 37}{space 1}   -0.82{col 46}{space 3}0.411{col 54}{space 4}-1.420891{col 67}{space 3}  .580734
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-1.040985{col 26}{space 2}  .521631{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-2.063363{col 67}{space 3}-.0186075
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} .3472789{col 26}{space 2} .5293378{col 37}{space 1}    0.66{col 46}{space 3}0.512{col 54}{space 4}-.6902041{col 67}{space 3} 1.384762
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0011323{col 26}{space 2} .0075251{col 37}{space 1}    0.15{col 46}{space 3}0.880{col 54}{space 4}-.0136167{col 67}{space 3} .0158813
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0036785{col 26}{space 2} .0068773{col 37}{space 1}    0.53{col 46}{space 3}0.593{col 54}{space 4}-.0098008{col 67}{space 3} .0171577
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0127081{col 26}{space 2} .0072199{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0014426{col 67}{space 3} .0268589
{txt}expert_ideol {c |}{col 14}{res}{space 2}-.0061059{col 26}{space 2} .0071477{col 37}{space 1}   -0.85{col 46}{space 3}0.393{col 54}{space 4}-.0201151{col 67}{space 3} .0079034
{txt}health_frame {c |}{col 14}{res}{space 2} .1528225{col 26}{space 2} .1146177{col 37}{space 1}    1.33{col 46}{space 3}0.182{col 54}{space 4} -.071824{col 67}{space 3}  .377469
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0089626{col 26}{space 2} .0049579{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0007547{col 67}{space 3} .0186799
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2811577{col 26}{space 2} .1266502{col 37}{space 1}    2.22{col 46}{space 3}0.026{col 54}{space 4} .0329279{col 67}{space 3} .5293874
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0517353{col 26}{space 2} .1390726{col 37}{space 1}    0.37{col 46}{space 3}0.710{col 54}{space 4} -.220842{col 67}{space 3} .3243125
{txt}{space 7}white {c |}{col 14}{res}{space 2}  .368838{col 26}{space 2} .1359001{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1024787{col 67}{space 3} .6351974
{txt}{space 6}income {c |}{col 14}{res}{space 2} .1520717{col 26}{space 2} .0275859{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .0980043{col 67}{space 3} .2061391
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6308188{col 26}{space 2} .1164686{col 37}{space 1}   -5.42{col 46}{space 3}0.000{col 54}{space 4}-.8590931{col 67}{space 3}-.4025445
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.856508{col 26}{space 2} .4399299{col 54}{space 4}-3.718755{col 67}{space 3}-1.994261
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.820634{col 26}{space 2} .4091697{col 54}{space 4}-2.622591{col 67}{space 3}-1.018676
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.9062839{col 26}{space 2} .3999561{col 54}{space 4}-1.690183{col 67}{space 3}-.1223844
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} .7010292{col 26}{space 2} .3985304{col 54}{space 4} -.080076{col 67}{space 3} 1.482134
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}    12,500
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}     2,500
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}    10,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .2087
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .001934
{col 63}{txt}avg ={col 69}{res}    .00721
{txt}Log marginal-likelihood = {res}-1443.0617{col 63}{txt}max ={col 69}{res}    .01992
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1}-.2031373{col 25}{space 2} .0575608{col 36}{space 2} .009924{col 46}{space 2}-.1995284{col 57}{space 2}-.3204942{col 68}{space 2}-.0951248
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.5217073{col 25}{space 2} .0693196{col 36}{space 2} .006162{col 46}{space 2}-.5233759{col 57}{space 2}-.6548195{col 68}{space 2}-.3924341
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-1.117572{col 25}{space 2} .0254509{col 36}{space 2} .004172{col 46}{space 2}-1.119197{col 57}{space 2}-1.166209{col 68}{space 2}-1.066383
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} .2100205{col 25}{space 2} .0962563{col 36}{space 2} .019699{col 46}{space 2} .2120286{col 57}{space 2} .0002062{col 68}{space 2} .3783906
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0024318{col 25}{space 2} .0025499{col 36}{space 2} .000216{col 46}{space 2} .0024405{col 57}{space 2}-.0026694{col 68}{space 2}  .007377
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0052084{col 25}{space 2} .0025604{col 36}{space 2} .000228{col 46}{space 2} .0051942{col 57}{space 2} .0001027{col 68}{space 2} .0101262
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1}  .013682{col 25}{space 2} .0027189{col 36}{space 2} .000243{col 46}{space 2} .0136375{col 57}{space 2} .0083454{col 68}{space 2} .0189083
{txt}expert_ideol {c |}{col 14}{res}{space 1} -.004328{col 25}{space 2} .0027761{col 36}{space 2} .000275{col 46}{space 2}-.0043765{col 57}{space 2}-.0095759{col 68}{space 2} .0011692
{txt}health_frame {c |}{col 14}{res}{space 1} .1210764{col 25}{space 2} .0381157{col 36}{space 2} .006897{col 46}{space 2} .1211722{col 57}{space 2}  .041111{col 68}{space 2} .1934812
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0086668{col 25}{space 2} .0021675{col 36}{space 2} .000154{col 46}{space 2} .0086377{col 57}{space 2} .0043485{col 68}{space 2}   .01286
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1}  .239135{col 25}{space 2} .0613023{col 36}{space 2} .012713{col 46}{space 2} .2386589{col 57}{space 2} .1155946{col 68}{space 2} .3612613
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0208526{col 25}{space 2} .0213947{col 36}{space 2} .004106{col 46}{space 2} .0214406{col 57}{space 2}-.0218705{col 68}{space 2} .0633447
{txt}{space 7}white {c |}{col 14}{res}{space 1} .3715271{col 25}{space 2} .0298432{col 36}{space 2} .002559{col 46}{space 2} .3720818{col 57}{space 2} .3135103{col 68}{space 2} .4295251
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1614272{col 25}{space 2}  .026127{col 36}{space 2} .002281{col 46}{space 2}  .161751{col 57}{space 2}  .111353{col 68}{space 2} .2126841
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6161651{col 25}{space 2} .0713372{col 36}{space 2}  .01622{col 46}{space 2}-.6172273{col 57}{space 2}-.7402583{col 68}{space 2}-.4566385
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.957678{col 25}{space 2} .0324463{col 36}{space 2} .007251{col 46}{space 2}-2.958072{col 57}{space 2}-3.027165{col 68}{space 2}-2.889496
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1}-1.870571{col 25}{space 2} .0686083{col 36}{space 2} .014539{col 46}{space 2}-1.865569{col 57}{space 2}-2.003394{col 68}{space 2}-1.739075
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1}-.9322978{col 25}{space 2} .0733469{col 36}{space 2} .016177{col 46}{space 2}-.9329105{col 57}{space 2}-1.074603{col 68}{space 2}-.7742806
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6738128{col 25}{space 2} .0841154{col 36}{space 2} .016157{col 46}{space 2} .6721331{col 57}{space 2}  .507641{col 68}{space 2}  .842267
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. bayes, mcmcsize(20000) burnin(5000) saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}    25,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}     5,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}    20,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .2045
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .001298
{col 63}{txt}avg ={col 69}{res}   .006101
{txt}Log marginal-likelihood = {res}-1439.4426{col 63}{txt}max ={col 69}{res}    .02179
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1} -.194907{col 25}{space 2}  .064837{col 36}{space 2} .009079{col 46}{space 2}-.1899765{col 57}{space 2}-.3317674{col 68}{space 2}-.0747162
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.5387363{col 25}{space 2} .0814403{col 36}{space 2} .009888{col 46}{space 2}-.5383898{col 57}{space 2}-.7084366{col 68}{space 2}-.3890303
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-1.141596{col 25}{space 2} .0371743{col 36}{space 2} .006741{col 46}{space 2}-1.138651{col 57}{space 2}-1.215485{col 68}{space 2}-1.074194
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} .1754617{col 25}{space 2} .1076221{col 36}{space 2} .017412{col 46}{space 2} .1817112{col 57}{space 2}-.0628625{col 68}{space 2} .3741783
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0021359{col 25}{space 2} .0026367{col 36}{space 2}  .00016{col 46}{space 2} .0021071{col 57}{space 2}-.0031892{col 68}{space 2} .0073335
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0051293{col 25}{space 2} .0026428{col 36}{space 2} .000169{col 46}{space 2} .0050668{col 57}{space 2}-.0001359{col 68}{space 2} .0103828
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1} .0139079{col 25}{space 2} .0026964{col 36}{space 2} .000165{col 46}{space 2}  .013903{col 57}{space 2}  .008509{col 68}{space 2} .0190686
{txt}expert_ideol {c |}{col 14}{res}{space 1}-.0040646{col 25}{space 2} .0028341{col 36}{space 2} .000239{col 46}{space 2}-.0040598{col 57}{space 2}-.0094367{col 68}{space 2} .0016702
{txt}health_frame {c |}{col 14}{res}{space 1} .1213932{col 25}{space 2} .0395396{col 36}{space 2} .005163{col 46}{space 2} .1206879{col 57}{space 2} .0406584{col 68}{space 2} .1990795
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1}  .008965{col 25}{space 2} .0021623{col 36}{space 2} .000104{col 46}{space 2} .0089388{col 57}{space 2} .0047866{col 68}{space 2} .0133166
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .2318881{col 25}{space 2} .0618639{col 36}{space 2} .009029{col 46}{space 2} .2323574{col 57}{space 2} .1080904{col 68}{space 2} .3494009
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0040364{col 25}{space 2} .0253923{col 36}{space 2} .004045{col 46}{space 2} .0050575{col 57}{space 2}-.0459876{col 68}{space 2} .0506468
{txt}{space 7}white {c |}{col 14}{res}{space 1}   .36488{col 25}{space 2} .0313734{col 36}{space 2} .002534{col 46}{space 2}  .365127{col 57}{space 2} .3046378{col 68}{space 2}  .428067
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1627335{col 25}{space 2} .0254646{col 36}{space 2} .001545{col 46}{space 2} .1627221{col 57}{space 2} .1133733{col 68}{space 2} .2136119
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6140977{col 25}{space 2}  .067418{col 36}{space 2} .010725{col 46}{space 2}-.6143805{col 57}{space 2}-.7430138{col 68}{space 2}-.4688467
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.972221{col 25}{space 2} .0499155{col 36}{space 2} .009795{col 46}{space 2}-2.973977{col 57}{space 2}-3.069815{col 68}{space 2}-2.870255
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1}-1.885884{col 25}{space 2}  .073494{col 36}{space 2} .011767{col 46}{space 2}-1.886488{col 57}{space 2}-2.019742{col 68}{space 2}-1.737231
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1}-.9411874{col 25}{space 2} .0706208{col 36}{space 2}  .01063{col 46}{space 2} -.943185{col 57}{space 2}-1.079562{col 68}{space 2}-.8026587
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6683282{col 25}{space 2} .0835496{col 36}{space 2} .011487{col 46}{space 2} .6643154{col 57}{space 2} .5043132{col 68}{space 2}  .832687
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. bayes, mcmcsize(100000) burnin(20000) saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}   120,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}    20,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   100,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}      .203
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}    .00104
{col 63}{txt}avg ={col 69}{res}    .00369
{txt}Log marginal-likelihood = {res}-1430.4625{col 63}{txt}max ={col 69}{res}    .02059
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1}-.1567449{col 25}{space 2} .0902692{col 36}{space 2} .007459{col 46}{space 2}-.1522697{col 57}{space 2}-.3393372{col 68}{space 2} .0145835
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.5194918{col 25}{space 2} .1026283{col 36}{space 2} .008053{col 46}{space 2}-.5192117{col 57}{space 2}-.7174946{col 68}{space 2}-.3287017
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-1.112863{col 25}{space 2} .0785297{col 36}{space 2} .007577{col 46}{space 2}-1.102103{col 57}{space 2}-1.257657{col 68}{space 2}-.9661916
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1}  .164207{col 25}{space 2} .1378404{col 36}{space 2} .011431{col 46}{space 2} .1673447{col 57}{space 2}-.1107499{col 68}{space 2}  .446391
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0015885{col 25}{space 2} .0026905{col 36}{space 2} .000112{col 46}{space 2} .0015684{col 57}{space 2} -.003716{col 68}{space 2}  .006863
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0048322{col 25}{space 2} .0027181{col 36}{space 2} .000121{col 46}{space 2} .0047697{col 57}{space 2}-.0004698{col 68}{space 2} .0103159
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1} .0135639{col 25}{space 2} .0026936{col 36}{space 2} .000116{col 46}{space 2} .0135475{col 57}{space 2} .0083888{col 68}{space 2} .0189174
{txt}expert_ideol {c |}{col 14}{res}{space 1}-.0038358{col 25}{space 2} .0029741{col 36}{space 2} .000143{col 46}{space 2}-.0038629{col 57}{space 2}-.0095892{col 68}{space 2}  .001978
{txt}health_frame {c |}{col 14}{res}{space 1} .1483637{col 25}{space 2} .0528735{col 36}{space 2} .004338{col 46}{space 2} .1453422{col 57}{space 2} .0479323{col 68}{space 2} .2570889
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0090447{col 25}{space 2} .0020775{col 36}{space 2} .000046{col 46}{space 2} .0090368{col 57}{space 2} .0049786{col 68}{space 2} .0131833
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .2467356{col 25}{space 2} .0787553{col 36}{space 2} .006453{col 46}{space 2} .2429597{col 57}{space 2} .0958688{col 68}{space 2} .4126401
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0259302{col 25}{space 2}  .032777{col 36}{space 2} .002794{col 46}{space 2} .0266697{col 57}{space 2}-.0362855{col 68}{space 2} .0870355
{txt}{space 7}white {c |}{col 14}{res}{space 1} .3373611{col 25}{space 2} .0323768{col 36}{space 2} .001836{col 46}{space 2} .3370827{col 57}{space 2} .2749448{col 68}{space 2} .4010094
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1605099{col 25}{space 2} .0246275{col 36}{space 2}  .00085{col 46}{space 2} .1604952{col 57}{space 2} .1117835{col 68}{space 2} .2092878
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6278838{col 25}{space 2} .0749502{col 36}{space 2} .005806{col 46}{space 2}-.6243624{col 57}{space 2}-.7867048{col 68}{space 2}-.4922843
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.874123{col 25}{space 2} .1201898{col 36}{space 2} .011784{col 46}{space 2}-2.869085{col 57}{space 2} -3.07842{col 68}{space 2}-2.620292
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1}-1.821431{col 25}{space 2} .1130632{col 36}{space 2} .010138{col 46}{space 2}-1.820365{col 57}{space 2} -2.03786{col 68}{space 2}-1.602767
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1}-.9255346{col 25}{space 2} .0820974{col 36}{space 2} .006558{col 46}{space 2}-.9259475{col 57}{space 2}-1.093581{col 68}{space 2}-.7667686
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6705087{col 25}{space 2}  .092149{col 36}{space 2} .006439{col 46}{space 2} .6711875{col 57}{space 2} .4829109{col 68}{space 2} .8550297
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. bayes, mcmcsize(200000) burnin(20000) saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}    20,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   200,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .2052
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .001031
{col 63}{txt}avg ={col 69}{res}   .003369
{txt}Log marginal-likelihood = {res}-1426.4424{col 63}{txt}max ={col 69}{res}    .02002
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1}-.1507365{col 25}{space 2} .0932624{col 36}{space 2} .005533{col 46}{space 2}-.1474407{col 57}{space 2}-.3379609{col 68}{space 2} .0252912
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.5974438{col 25}{space 2} .1297934{col 36}{space 2} .007986{col 46}{space 2}-.5970064{col 57}{space 2}-.8358417{col 68}{space 2}-.3538302
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-1.108761{col 25}{space 2} .0841566{col 36}{space 2} .005783{col 46}{space 2} -1.10267{col 57}{space 2}-1.281705{col 68}{space 2} -.958924
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} .1643933{col 25}{space 2} .1424191{col 36}{space 2} .008495{col 46}{space 2} .1663312{col 57}{space 2}-.1114294{col 68}{space 2}  .446391
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0015458{col 25}{space 2} .0027409{col 36}{space 2} .000083{col 46}{space 2} .0015332{col 57}{space 2}-.0038495{col 68}{space 2} .0069113
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0058601{col 25}{space 2} .0029196{col 36}{space 2} .000112{col 46}{space 2} .0058457{col 57}{space 2} .0002858{col 68}{space 2} .0115933
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1} .0135227{col 25}{space 2} .0027733{col 36}{space 2} .000089{col 46}{space 2} .0135121{col 57}{space 2} .0081094{col 68}{space 2} .0190072
{txt}expert_ideol {c |}{col 14}{res}{space 1}-.0038574{col 25}{space 2}  .003061{col 36}{space 2} .000108{col 46}{space 2}-.0038724{col 57}{space 2}-.0098371{col 68}{space 2} .0020343
{txt}health_frame {c |}{col 14}{res}{space 1} .1350651{col 25}{space 2} .0541915{col 36}{space 2} .003163{col 46}{space 2} .1344197{col 57}{space 2} .0319876{col 68}{space 2} .2441902
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0090454{col 25}{space 2} .0020778{col 36}{space 2} .000033{col 46}{space 2} .0090396{col 57}{space 2} .0050053{col 68}{space 2} .0131801
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .2223972{col 25}{space 2} .0837721{col 36}{space 2} .004964{col 46}{space 2} .2224073{col 57}{space 2} .0595507{col 68}{space 2} .3930365
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0459925{col 25}{space 2} .0465769{col 36}{space 2} .003052{col 46}{space 2} .0404852{col 57}{space 2}  -.03067{col 68}{space 2} .1456336
{txt}{space 7}white {c |}{col 14}{res}{space 1}   .34876{col 25}{space 2} .0391644{col 36}{space 2} .002011{col 46}{space 2} .3477036{col 57}{space 2}  .275111{col 68}{space 2} .4276939
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1604857{col 25}{space 2} .0249808{col 36}{space 2} .000626{col 46}{space 2} .1604974{col 57}{space 2} .1110837{col 68}{space 2}  .209426
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6079579{col 25}{space 2} .0779761{col 36}{space 2} .004379{col 46}{space 2}-.6085731{col 57}{space 2}-.7670261{col 68}{space 2}-.4572949
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.866738{col 25}{space 2} .1270371{col 36}{space 2} .008846{col 46}{space 2}-2.832503{col 57}{space 2}-3.142615{col 68}{space 2}-2.670614
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1}-1.795377{col 25}{space 2} .1205198{col 36}{space 2} .007733{col 46}{space 2}-1.783864{col 57}{space 2}-2.043348{col 68}{space 2}-1.589834
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1}-.8914789{col 25}{space 2} .0913548{col 36}{space 2} .005415{col 46}{space 2}-.8919445{col 57}{space 2}-1.070374{col 68}{space 2}-.7203352
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6914477{col 25}{space 2} .0959173{col 36}{space 2} .004897{col 46}{space 2} .6918083{col 57}{space 2} .5016581{col 68}{space 2} .8787717
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. bayes, mcmcsize(500000) burnin(50000) saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}   550,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}    50,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   500,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .2054
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .001021
{col 63}{txt}avg ={col 69}{res}   .002555
{txt}Log marginal-likelihood = {res}-1421.3917{col 63}{txt}max ={col 69}{res}   .007904
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1}-.1792511{col 25}{space 2}  .100008{col 36}{space 2}   .0039{col 46}{space 2}-.1806219{col 57}{space 2}-.3760967{col 68}{space 2} .0161582
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.7108113{col 25}{space 2} .1406518{col 36}{space 2} .005618{col 46}{space 2}-.7162242{col 57}{space 2}-.9722779{col 68}{space 2}-.4320003
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-1.133649{col 25}{space 2} .1044677{col 36}{space 2} .004591{col 46}{space 2}-1.132826{col 57}{space 2}-1.329179{col 68}{space 2}-.9198952
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} .1114085{col 25}{space 2} .1418084{col 36}{space 2}  .00542{col 46}{space 2} .1082286{col 57}{space 2}-.1554341{col 68}{space 2} .3919976
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0018539{col 25}{space 2}  .002776{col 36}{space 2} .000057{col 46}{space 2} .0018346{col 57}{space 2}-.0035414{col 68}{space 2}  .007346
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0072427{col 25}{space 2}  .002962{col 36}{space 2} .000073{col 46}{space 2}  .007248{col 57}{space 2} .0014629{col 68}{space 2} .0130178
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1} .0137757{col 25}{space 2} .0028552{col 36}{space 2} .000065{col 46}{space 2} .0137638{col 57}{space 2} .0081677{col 68}{space 2} .0194354
{txt}expert_ideol {c |}{col 14}{res}{space 1}-.0032345{col 25}{space 2}  .003029{col 36}{space 2} .000068{col 46}{space 2} -.003227{col 57}{space 2} -.009186{col 68}{space 2} .0026579
{txt}health_frame {c |}{col 14}{res}{space 1}  .133812{col 25}{space 2} .0528502{col 36}{space 2} .001946{col 46}{space 2} .1318819{col 57}{space 2} .0337476{col 68}{space 2} .2417939
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0084148{col 25}{space 2} .0021526{col 36}{space 2} .000034{col 46}{space 2} .0084103{col 57}{space 2} .0042052{col 68}{space 2} .0126641
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .2011214{col 25}{space 2} .0792776{col 36}{space 2} .002942{col 46}{space 2} .1987135{col 57}{space 2}  .050859{col 68}{space 2} .3620653
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .1151503{col 25}{space 2} .0628367{col 36}{space 2} .002708{col 46}{space 2}  .122237{col 57}{space 2}-.0080686{col 68}{space 2} .2311705
{txt}{space 7}white {c |}{col 14}{res}{space 1} .3593543{col 25}{space 2} .0532837{col 36}{space 2} .002059{col 46}{space 2} .3560137{col 57}{space 2} .2599764{col 68}{space 2} .4672927
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1581308{col 25}{space 2}  .024838{col 36}{space 2} .000408{col 46}{space 2} .1579882{col 57}{space 2} .1097629{col 68}{space 2} .2070602
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.5786989{col 25}{space 2} .0784557{col 36}{space 2} .002854{col 46}{space 2}-.5795287{col 57}{space 2}-.7318704{col 68}{space 2}-.4271631
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.791267{col 25}{space 2} .1447787{col 36}{space 2} .006407{col 46}{space 2}-2.785868{col 57}{space 2}-3.083184{col 68}{space 2} -2.50693
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1}-1.769313{col 25}{space 2} .1321184{col 36}{space 2} .005489{col 46}{space 2} -1.76684{col 57}{space 2} -2.03563{col 68}{space 2}-1.511274
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1}-.8705266{col 25}{space 2} .0861037{col 36}{space 2} .003183{col 46}{space 2}-.8710511{col 57}{space 2}-1.043214{col 68}{space 2}-.7046269
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6985541{col 25}{space 2} .0924799{col 36}{space 2} .002962{col 46}{space 2}  .698467{col 57}{space 2} .5179279{col 68}{space 2} .8823022
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter gop white income gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1274.1579}  
Iteration 2:{space 3}log likelihood = {res:-1273.5111}  
Iteration 3:{space 3}log likelihood = {res:-1273.5108}  
Iteration 4:{space 3}log likelihood = {res:-1273.5108}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     99.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1273.5108{txt}{col 49}Pseudo R2{col 67}= {res}    0.0377

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1072363{col 26}{space 2} .5644748{col 37}{space 1}   -0.19{col 46}{space 3}0.849{col 54}{space 4}-1.213587{col 67}{space 3} .9991139
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.4231216{col 26}{space 2} .5115594{col 37}{space 1}   -0.83{col 46}{space 3}0.408{col 54}{space 4} -1.42576{col 67}{space 3} .5795163
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-1.048931{col 26}{space 2} .5218198{col 37}{space 1}   -2.01{col 46}{space 3}0.044{col 54}{space 4}-2.071679{col 67}{space 3}-.0261828
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} .3617873{col 26}{space 2} .5303963{col 37}{space 1}    0.68{col 46}{space 3}0.495{col 54}{space 4}-.6777704{col 67}{space 3} 1.401345
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 2} .0008856{col 26}{space 2} .0075338{col 37}{space 1}    0.12{col 46}{space 3}0.906{col 54}{space 4}-.0138803{col 67}{space 3} .0156516
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 2} .0036453{col 26}{space 2} .0068871{col 37}{space 1}    0.53{col 46}{space 3}0.597{col 54}{space 4}-.0098531{col 67}{space 3} .0171437
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 2} .0127372{col 26}{space 2} .0072209{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0014154{col 67}{space 3} .0268899
{txt}expert_ideol {c |}{col 14}{res}{space 2}  -.00637{col 26}{space 2} .0071622{col 37}{space 1}   -0.89{col 46}{space 3}0.374{col 54}{space 4}-.0204076{col 67}{space 3} .0076676
{txt}health_frame {c |}{col 14}{res}{space 2} .1409655{col 26}{space 2} .1150001{col 37}{space 1}    1.23{col 46}{space 3}0.220{col 54}{space 4}-.0844306{col 67}{space 3} .3663615
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0089378{col 26}{space 2} .0049697{col 37}{space 1}    1.80{col 46}{space 3}0.072{col 54}{space 4}-.0008026{col 67}{space 3} .0186781
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2765956{col 26}{space 2} .1267728{col 37}{space 1}    2.18{col 46}{space 3}0.029{col 54}{space 4} .0281254{col 67}{space 3} .5250657
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0508792{col 26}{space 2} .1391457{col 37}{space 1}    0.37{col 46}{space 3}0.715{col 54}{space 4}-.2218415{col 67}{space 3} .3235998
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.1631509{col 26}{space 2} .1253519{col 37}{space 1}   -1.30{col 46}{space 3}0.193{col 54}{space 4}-.4088362{col 67}{space 3} .0825343
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4070029{col 26}{space 2} .1392409{col 37}{space 1}    2.92{col 46}{space 3}0.003{col 54}{space 4} .1340957{col 67}{space 3} .6799101
{txt}{space 6}income {c |}{col 14}{res}{space 2}  .154831{col 26}{space 2} .0276868{col 37}{space 1}    5.59{col 46}{space 3}0.000{col 54}{space 4} .1005658{col 67}{space 3} .2090961
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6223273{col 26}{space 2} .1166697{col 37}{space 1}   -5.33{col 46}{space 3}0.000{col 54}{space 4}-.8509958{col 67}{space 3}-.3936588
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.882632{col 26}{space 2} .4410909{col 54}{space 4}-3.747155{col 67}{space 3} -2.01811
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.846124{col 26}{space 2} .4103812{col 54}{space 4}-2.650456{col 67}{space 3}-1.041791
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.9308387{col 26}{space 2} .4011289{col 54}{space 4}-1.717037{col 67}{space 3}-.1446406
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} .6782245{col 26}{space 2} .3995946{col 54}{space 4}-.1049665{col 67}{space 3} 1.461415
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, mcmcsize(500000) burnin(50000) saving(simdata) rseed(32306): ologit distance cdc_m pres_m state_m expert_m cdc_ideol pres_ideol state_ideol expert_ideol health_frame ideology_rs jobloss shelter gop white income gender
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 13}{space 2}{res:distance} ~ ologit(xb_distance,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 28}{space 9}{res}{c -(}distance:cdc_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:pres_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:state_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 6}{res}{c -(}distance:expert_m{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 5}{res}{c -(}distance:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}distance:pres_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:state_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:expert_ideol{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 2}{res}{c -(}distance:health_frame{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 3}{res}{c -(}distance:ideology_rs{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:jobloss{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 7}{res}{c -(}distance:shelter{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 11}{res}{c -(}distance:gop{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 9}{res}{c -(}distance:white{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:income{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 8}{res}{c -(}distance:gender{c )-}{txt} ~ normal(0,10000){space 32}(1){p_end}
{p 0 28}{space 4}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_distance.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}   550,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}    50,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   500,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .2608
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .001139
{col 63}{txt}avg ={col 69}{res}   .004251
{txt}Log marginal-likelihood = {res}-1468.1892{col 63}{txt}max ={col 69}{res}    .01532
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}distance     {txt}{c |}
{space 7}cdc_m {c |}{col 14}{res}{space 1}-.1074093{col 25}{space 2} .1207067{col 36}{space 2} .005059{col 46}{space 2}-.1013185{col 57}{space 2}-.3782575{col 68}{space 2} .1075252
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.4514316{col 25}{space 2} .0688018{col 36}{space 2} .002172{col 46}{space 2}-.4494587{col 57}{space 2}-.5922195{col 68}{space 2}-.3205826
{txt}{space 5}state_m {c |}{col 14}{res}{space 1} -1.08918{col 25}{space 2} .0961154{col 36}{space 2} .003572{col 46}{space 2}-1.083851{col 57}{space 2}-1.295217{col 68}{space 2}-.9128225
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1} .3402616{col 25}{space 2}  .134667{col 36}{space 2} .002058{col 46}{space 2} .3394651{col 57}{space 2} .0771536{col 68}{space 2} .6051116
{txt}{space 3}cdc_ideol {c |}{col 14}{res}{space 1} .0008401{col 25}{space 2} .0029639{col 36}{space 2} .000093{col 46}{space 2} .0007998{col 57}{space 2}-.0048691{col 68}{space 2} .0068276
{txt}{space 2}pres_ideol {c |}{col 14}{res}{space 1} .0039664{col 25}{space 2} .0025606{col 36}{space 2} .000044{col 46}{space 2} .0039567{col 57}{space 2}-.0010088{col 68}{space 2} .0090116
{txt}{space 1}state_ideol {c |}{col 14}{res}{space 1} .0131966{col 25}{space 2} .0028371{col 36}{space 2} .000056{col 46}{space 2} .0131653{col 57}{space 2} .0076666{col 68}{space 2}  .018825
{txt}expert_ideol {c |}{col 14}{res}{space 1}-.0061296{col 25}{space 2} .0029881{col 36}{space 2} .000034{col 46}{space 2}-.0061221{col 57}{space 2} -.011961{col 68}{space 2}-.0002662
{txt}health_frame {c |}{col 14}{res}{space 1} .1375033{col 25}{space 2} .0816184{col 36}{space 2} .001281{col 46}{space 2}   .13756{col 57}{space 2}-.0228955{col 68}{space 2}   .29829
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0085028{col 25}{space 2} .0021033{col 36}{space 2}  .00006{col 46}{space 2} .0085261{col 57}{space 2} .0043032{col 68}{space 2} .0125393
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1} .3029487{col 25}{space 2} .0634178{col 36}{space 2} .002407{col 46}{space 2} .3028545{col 57}{space 2} .1794983{col 68}{space 2} .4241735
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0838807{col 25}{space 2} .0636048{col 36}{space 2} .002145{col 46}{space 2} .0802364{col 57}{space 2}-.0329076{col 68}{space 2} .2205133
{txt}{space 9}gop {c |}{col 14}{res}{space 1}-.1628022{col 25}{space 2} .0835544{col 36}{space 2} .002326{col 46}{space 2}-.1625259{col 57}{space 2}-.3281478{col 68}{space 2} .0007207
{txt}{space 7}white {c |}{col 14}{res}{space 1}  .391749{col 25}{space 2} .0406035{col 36}{space 2} .000875{col 46}{space 2} .3918936{col 57}{space 2} .3118044{col 68}{space 2} .4708554
{txt}{space 6}income {c |}{col 14}{res}{space 1} .1593823{col 25}{space 2} .0228397{col 36}{space 2} .000571{col 46}{space 2} .1591821{col 57}{space 2} .1153448{col 68}{space 2} .2050041
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6595189{col 25}{space 2} .0512141{col 36}{space 2} .001323{col 46}{space 2}-.6595838{col 57}{space 2} -.759016{col 68}{space 2}-.5590231
{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 8}cut1 {c |}{col 14}{res}{space 1}-2.870873{col 25}{space 2} .0391839{col 36}{space 2} .001065{col 46}{space 2} -2.87078{col 57}{space 2} -2.94806{col 68}{space 2}-2.794454
{txt}{space 8}cut2 {c |}{col 14}{res}{space 1} -1.87208{col 25}{space 2}  .068604{col 36}{space 2} .001207{col 46}{space 2}   -1.872{col 57}{space 2}-2.006005{col 68}{space 2}-1.737642
{txt}{space 8}cut3 {c |}{col 14}{res}{space 1} -.985679{col 25}{space 2}   .04431{col 36}{space 2} .001571{col 46}{space 2}-.9830475{col 57}{space 2}-1.078676{col 68}{space 2}-.9050906
{txt}{space 8}cut4 {c |}{col 14}{res}{space 1} .6538292{col 25}{space 2} .0691029{col 36}{space 2} .001412{col 46}{space 2} .6539987{col 57}{space 2} .5179711{col 68}{space 2}  .789375
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata.dta

. anova no_shop cdc_econ cdc_health pres_health pres_econ state_econ state_health expert_health expert_econ control_econ control_health
{err}no observations
{txt}{search r(2000), local:r(2000);}

{com}. gen t1 = cdc_econ
{txt}(1,214 missing values generated)

{com}. gen t2 = cdc_health
{txt}(1,224 missing values generated)

{com}. gen t3 = pres_health
{txt}(1,214 missing values generated)

{com}. gen t4 = pres_econ
{txt}(1,197 missing values generated)

{com}. gen t5= state_econ
{txt}(1,209 missing values generated)

{com}. gen t6 = state_health
{txt}(1,229 missing values generated)

{com}. gen t7 = expert_health
{txt}(1,220 missing values generated)

{com}. gen t8 = expert_econ
{txt}(1,201 missing values generated)

{com}. gen c1= control_econ
{txt}(1,200 missing values generated)

{com}. gen c2 = control_health
{txt}(1,226 missing values generated)

{com}. recode t1- c2 .=0
{err}, invalid name
{txt}{search r(198), local:r(198);}

{com}. recode t1 t2 t3 t4 t5 t6 t7 t8 c1 c2 .=0
{err}unknown el t2 in rule
{txt}{search r(198), local:r(198);}

{com}. recode t1 .=0
{txt}(t1: 1214 changes made)

{com}. recode t2 .=0
{txt}(t2: 1224 changes made)

{com}. recode t3 .=0
{txt}(t3: 1214 changes made)

{com}. recode t4 .=0
{txt}(t4: 1197 changes made)

{com}. recode t5 .=0
{txt}(t5: 1209 changes made)

{com}. recode t6 .=0
{txt}(t6: 1229 changes made)

{com}. recode t7 .=0
{txt}(t7: 1220 changes made)

{com}. recode t8 .=0
{txt}(t8: 1201 changes made)

{com}. recode c1 .=0
{txt}(c1: 1200 changes made)

{com}. recode c2 .=0
{txt}(c2: 1226 changes made)

{com}. anova t1 t2 t3 t4 t5 t6 t7 t8 c1 c2

                         {txt}Number of obs = {res}     1,348    {txt}R-squared     ={res}  0.9169
                         {txt}Root MSE      =   {res} .155741    {txt}Adj R-squared ={res}  0.9158

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 355.83592         18   19.768662    815.02  0.0000
                         {txt}{c |}
                      t2 {c |} {res} 188.74932          2   94.374661   3890.89  0.0000
{txt}                      t3 {c |} {res} 196.41656          2   98.208279   4048.94  0.0000
{txt}                      t4 {c |} {res} 208.22464          2   104.11232   4292.35  0.0000
{txt}                      t5 {c |} {res} 200.04107          2   100.02053   4123.66  0.0000
{txt}                      t6 {c |} {res}  184.6902          2   92.345098   3807.21  0.0000
{txt}                      t7 {c |} {res} 191.88592          2   95.942959   3955.55  0.0000
{txt}                      t8 {c |} {res} 205.57389          2   102.78695   4237.71  0.0000
{txt}                      c1 {c |} {res} 206.24358          2   103.12179   4251.52  0.0000
{txt}                      c2 {c |} {res} 187.14455          2   93.572275   3857.81  0.0000
                         {txt}{c |}
                Residual {c |} {res} 32.235294      1,329    .0242553  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 388.07122      1,347   .28810038  

{com}. anova no_shop t1 t2 t3 t4 t5 t6 t7 t8 c1 c2

                         {txt}Number of obs = {res}     1,348    {txt}R-squared     ={res}  1.0000
                         {txt}Root MSE      =   {res}       0    {txt}Adj R-squared ={res}  1.0000

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 259.12463         20   12.956231  
                         {txt}{c |}
                      t1 {c |} {res} 27.382353          2   13.691176  
{txt}                      t2 {c |} {res} 18.801587          2   9.4007937  
{txt}                      t3 {c |} {res} 20.404412          2   10.202206  
{txt}                      t4 {c |} {res} 35.503268          2   17.751634  
{txt}                      t5 {c |} {res} 30.269504          2   15.134752  
{txt}                      t6 {c |} {res} 20.975207          2   10.487603  
{txt}                      t7 {c |} {res} 26.469231          2   13.234615  
{txt}                      t8 {c |} {res} 30.590604          2   15.295302  
{txt}                      c1 {c |} {res}     33.66          2       16.83  
{txt}                      c2 {c |} {res} 19.354839          2   9.6774194  
                         {txt}{c |}
                Residual {c |} {res}         0      1,327           0  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 259.12463      1,347   .19237166  

{com}. gen treatment = .
{txt}(1,348 missing values generated)

{com}. recode treatment .=1 if t1 == 1
{txt}(treatment: 36 changes made)

{com}. recode treatment .=1 if t1 == 2
{txt}(treatment: 98 changes made)

{com}. recode treatment .=2 if t2 == 2
{txt}(treatment: 103 changes made)

{com}. recode treatment .=2 if t2 == 1
{txt}(treatment: 21 changes made)

{com}. recode treatment .=3 if t3 == 1
{txt}(treatment: 23 changes made)

{com}. recode treatment .=3 if t3 == 2
{txt}(treatment: 111 changes made)

{com}. recode treatment .=4 if t4 == 2
{txt}(treatment: 97 changes made)

{com}. recode treatment .=4 if t4 == 1
{txt}(treatment: 54 changes made)

{com}. recode treatment .=5 if t5 == 1
{txt}(treatment: 42 changes made)

{com}. recode treatment .=5 if t5 == 2
{txt}(treatment: 97 changes made)

{com}. recode treatment .=6 if t6 == 2
{txt}(treatment: 94 changes made)

{com}. recode treatment .=6 if t6 == 1
{txt}(treatment: 25 changes made)

{com}. recode treatment .=7 if t7 == 1
{txt}(treatment: 35 changes made)

{com}. recode treatment .=7 if t7 == 2
{txt}(treatment: 93 changes made)

{com}. recode treatment .=8 if t8 == 2
{txt}(treatment: 106 changes made)

{com}. recode treatment .=8 if t8 == 1
{txt}(treatment: 41 changes made)

{com}. recode treatment .=9 if c1 == 1
{txt}(treatment: 49 changes made)

{com}. recode treatment .=9 if c1 == 2
{txt}(treatment: 99 changes made)

{com}. recode treatment .=10 if c2 == 2
{txt}(treatment: 100 changes made)

{com}. recode treatment .=10 if c2 == 1
{txt}(treatment: 22 changes made)

{com}. sum treatment

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}treatment {c |}{res}      1,346    5.513373    2.855147          1         10

{com}. tab treatment

  {txt}treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        134        9.96        9.96
{txt}          2 {c |}{res}        124        9.21       19.17
{txt}          3 {c |}{res}        134        9.96       29.12
{txt}          4 {c |}{res}        151       11.22       40.34
{txt}          5 {c |}{res}        139       10.33       50.67
{txt}          6 {c |}{res}        119        8.84       59.51
{txt}          7 {c |}{res}        128        9.51       69.02
{txt}          8 {c |}{res}        147       10.92       79.94
{txt}          9 {c |}{res}        148       11.00       90.94
{txt}         10 {c |}{res}        122        9.06      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,346      100.00

{com}. anova no_shop treatment

                         {txt}Number of obs = {res}     1,346    {txt}R-squared     ={res}  0.0219
                         {txt}Root MSE      =   {res}  .43463    {txt}Adj R-squared ={res}  0.0153

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 5.6524798          9   .62805332      3.32  0.0005
                         {txt}{c |}
               treatment {c |} {res} 5.6524798          9   .62805332      3.32  0.0005
                         {txt}{c |}
                Residual {c |} {res} 252.37427      1,336   .18890289  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 258.02675      1,345   .19184145  

{com}. anova no_shop cdc_m pres_m state_m expert_m control_m

                         {txt}Number of obs = {res}     1,348    {txt}R-squared     ={res}  0.0062
                         {txt}Root MSE      =   {res} .438064    {txt}Adj R-squared ={res}  0.0025

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 1.5943926          5   .31887853      1.66  0.1409
                         {txt}{c |}
                   cdc_m {c |} {res} 1.2045617          1   1.2045617      6.28  0.0123
{txt}                  pres_m {c |} {res} 1.0578642          1   1.0578642      5.51  0.0190
{txt}                 state_m {c |} {res} 1.0876863          1   1.0876863      5.67  0.0174
{txt}                expert_m {c |} {res} 1.0397374          1   1.0397374      5.42  0.0201
{txt}               control_m {c |} {res} 1.0784586          1   1.0784586      5.62  0.0179
                         {txt}{c |}
                Residual {c |} {res} 257.53024      1,342   .19190033  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 259.12463      1,347   .19237166  

{com}. anova no_shop econ_frame health_frame

                         {txt}Number of obs = {res}     1,348    {txt}R-squared     ={res}  0.0193
                         {txt}Root MSE      =   {res} .434681    {txt}Adj R-squared ={res}  0.0178

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
             {hline 12}{c +}{hline 52}
                   Model {c |} {res} 4.9904049          2   2.4952025     13.21  0.0000
                         {txt}{c |}
              econ_frame {c |} {res} .95296866          1   .95296866      5.04  0.0249
{txt}             health_fr~e {c |} {res} 1.2728794          1   1.2728794      6.74  0.0095
                         {txt}{c |}
                Residual {c |} {res} 254.13422      1,345   .18894738  
             {txt}{hline 12}{c +}{hline 52}
                   Total {c |} {res} 259.12463      1,347   .19237166  

{com}. anova distance treatment

                         {txt}Number of obs = {res}     1,346    {txt}R-squared     ={res}  0.0045
                         {txt}Root MSE      =   {res} .878027    {txt}Adj R-squared ={res} -0.0022

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 4.6792971          9    .5199219      0.67  0.7327
                         {txt}{c |}
               treatment {c |} {res} 4.6792971          9    .5199219      0.67  0.7327
                         {txt}{c |}
                Residual {c |} {res} 1029.9633      1,336   .77093065  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 1034.6426      1,345   .76925104  

{com}. anova distance cdc_m pres_m state_m expert_m control_m

                         {txt}Number of obs = {res}     1,346    {txt}R-squared     ={res}  0.0016
                         {txt}Root MSE      =   {res} .877692    {txt}Adj R-squared ={res} -0.0014

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 1.6114757          4   .40286893      0.52  0.7189
                         {txt}{c |}
                   cdc_m {c |} {res} .13911303          1   .13911303      0.18  0.6709
{txt}                  pres_m {c |} {res} .10179126          1   .10179126      0.13  0.7163
{txt}                 state_m {c |} {res} .00076736          1   .00076736      0.00  0.9748
{txt}                expert_m {c |} {res} .70335712          1   .70335712      0.91  0.3395
{txt}               control_m {c |} {res}         0          0
                         {txt}{c |}
                Residual {c |} {res} 1033.0312      1,341    .7703439  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 1034.6426      1,345   .76925104  

{com}. anova distance econ_frame health_frame

                         {txt}Number of obs = {res}     1,346    {txt}R-squared     ={res}  0.0005
                         {txt}Root MSE      =   {res} .877189    {txt}Adj R-squared ={res} -0.0003

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
             {hline 12}{c +}{hline 52}
                   Model {c |} {res} .48851001          1   .48851001      0.63  0.4257
                         {txt}{c |}
              econ_frame {c |} {res} .48851001          1   .48851001      0.63  0.4257
{txt}             health_fr~e {c |} {res}         0          0
                         {txt}{c |}
                Residual {c |} {res} 1034.1541      1,344   .76945992  
             {txt}{hline 12}{c +}{hline 52}
                   Total {c |} {res} 1034.6426      1,345   .76925104  

{com}. oneway distance treatment

                        {txt}Analysis of Variance
    Source              SS         df      MS            F     Prob > F
{hline 72}
Between groups     {res} 4.67929711      9   .519921901      0.67     0.7327
{txt} Within groups     {res} 1029.96335   1336    .77093065
{txt}{hline 72}
    Total          {res} 1034.64264   1345   .769251037

{txt}Bartlett's test for equal variances:  chi2({res}9{txt}) = {res} 21.2768{txt}  Prob>chi2 = {res}0.011

{com}. oneway distance cdc_m pres_m state_m expert_m control_m
{err}too many variables specified
{txt}{search r(103), local:r(103);}

{com}. oneway treatment
{err}too few variables specified
{txt}{search r(102), local:r(102);}

{com}. oneway cdc_m control_m

                        {txt}Analysis of Variance
    Source              SS         df      MS            F     Prob > F
{hline 72}
Between groups     {res} 12.3678589      1   12.3678589     84.83     0.0000
{txt} Within groups     {res} 196.252319   1346     .1458041
{txt}{hline 72}
    Total          {res} 208.620178   1347   .154877638

{com}. anova no_shop treatment

                         {txt}Number of obs = {res}     1,346    {txt}R-squared     ={res}  0.0219
                         {txt}Root MSE      =   {res}  .43463    {txt}Adj R-squared ={res}  0.0153

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 5.6524798          9   .62805332      3.32  0.0005
                         {txt}{c |}
               treatment {c |} {res} 5.6524798          9   .62805332      3.32  0.0005
                         {txt}{c |}
                Residual {c |} {res} 252.37427      1,336   .18890289  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 258.02675      1,345   .19184145  

{com}. oneway no_shop treatment

                        {txt}Analysis of Variance
    Source              SS         df      MS            F     Prob > F
{hline 72}
Between groups     {res} 5.65247985      9   .628053317      3.32     0.0005
{txt} Within groups     {res} 252.374266   1336   .188902894
{txt}{hline 72}
    Total          {res} 258.026746   1345   .191841447

{txt}Bartlett's test for equal variances:  chi2({res}9{txt}) = {res} 20.3165{txt}  Prob>chi2 = {res}0.016

{com}. manova no_shop distance knowledge attention = treatment

                       {txt}Number of obs = {res}     1,346

                       {txt}W = Wilks' lambda      L = Lawley-Hotelling trace
                       P = Pillai's trace     R = Roy's largest root

              Source {c |} Statistic        df    F(df1,     df2) =   F   Prob>F
          {hline 11}{c +}{hline 55}
           treatment {c |}W {res}  0.9431         9     36.0   4997.1     2.19 0.0001 a{txt}
                     {c |}P {res}  0.0579               36.0   5344.0     2.18 0.0001 a{txt}
                     {c |}L {res}  0.0592               36.0   5326.0     2.19 0.0001 a{txt}
                     {c |}R {res}  0.0281                9.0   1336.0     4.17 0.0000 u{txt}
                     {c LT}{hline 55}
            Residual {c |}           {res}     1336{txt}
          {hline 11}{c +}{hline 55}
               Total {c |}           {res}     1345{txt}
          {hline 11}{c BT}{hline 55}
                       e = exact, a = approximate, u = upper bound on F

{com}. manova no_shop distance = treatment

                       {txt}Number of obs = {res}     1,346

                       {txt}W = Wilks' lambda      L = Lawley-Hotelling trace
                       P = Pillai's trace     R = Roy's largest root

              Source {c |} Statistic        df    F(df1,     df2) =   F   Prob>F
          {hline 11}{c +}{hline 55}
           treatment {c |}W {res}  0.9744         9     18.0   2670.0     1.94 0.0101 e{txt}
                     {c |}P {res}  0.0257               18.0   2672.0     1.93 0.0104 a{txt}
                     {c |}L {res}  0.0262               18.0   2668.0     1.94 0.0099 a{txt}
                     {c |}R {res}  0.0227                9.0   1336.0     3.36 0.0004 u{txt}
                     {c LT}{hline 55}
            Residual {c |}           {res}     1336{txt}
          {hline 11}{c +}{hline 55}
               Total {c |}           {res}     1345{txt}
          {hline 11}{c BT}{hline 55}
                       e = exact, a = approximate, u = upper bound on F

{com}. gen cdc_frame_e = econ_frame* cdc_m

. gen cdc_frame_h = health_frame* cdc_m

. gen pres_frame_e = econ_frame* pres_m

. gen pres_frame_h = health_frame* pres_m

. gen state_frame_e = econ_frame* state_m

. gen state_frame_h = health_frame* state_m

. gen expert_frame_e = econ_frame* expert_m

. gen expert_frame_h = health_frame* expert_m

. logit no_shop cdc_m pres_m state_m expert_m control_m shelter jobloss

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-767.86489}  
Iteration 2:{space 3}log likelihood = {res:-767.79772}  
Iteration 3:{space 3}log likelihood = {res:-767.78208}  
Iteration 4:{space 3}log likelihood = {res:-767.77882}  
Iteration 5:{space 3}log likelihood = {res:-767.77813}  
Iteration 6:{space 3}log likelihood = {res:-767.77798}  
Iteration 7:{space 3}log likelihood = {res:-767.77794}  
Iteration 8:{space 3}log likelihood = {res:-767.77793}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}7{txt}){col 67}= {res}      8.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.2986
{txt}Log likelihood = {res}-767.77793{txt}{col 49}Pseudo R2{col 67}= {res}    0.0054

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} 15.24257{col 26}{space 2} 767.8386{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1489.693{col 67}{space 3} 1520.179
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}   14.973{col 26}{space 2} 767.8386{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1489.963{col 67}{space 3} 1519.909
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} 15.02484{col 26}{space 2} 767.8386{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1489.911{col 67}{space 3} 1519.961
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2} 14.93975{col 26}{space 2} 767.8386{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1489.996{col 67}{space 3} 1519.876
{txt}{space 3}control_m {c |}{col 14}{res}{space 2} 15.01309{col 26}{space 2} 767.8386{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-1489.923{col 67}{space 3} 1519.949
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0546641{col 26}{space 2} .1528072{col 37}{space 1}    0.36{col 46}{space 3}0.721{col 54}{space 4}-.2448325{col 67}{space 3} .3541607
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2}-.0663434{col 26}{space 2} .1350712{col 37}{space 1}   -0.49{col 46}{space 3}0.623{col 54}{space 4} -.331078{col 67}{space 3} .1983913
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-14.00235{col 26}{space 2} 767.8386{col 37}{space 1}   -0.02{col 46}{space 3}0.985{col 54}{space 4}-1518.938{col 67}{space 3} 1490.934
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum no_shop

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}      1,348    .7403561    .4386019          0          1

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-759.97871}  
Iteration 2:{space 3}log likelihood = {res:-759.90675}  
Iteration 3:{space 3}log likelihood = {res:-759.90675}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}7{txt}){col 67}= {res}     24.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0011
{txt}Log likelihood = {res}-759.90675{txt}{col 49}Pseudo R2{col 67}= {res}    0.0156

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .2405444{col 26}{space 2} .2049458{col 37}{space 1}    1.17{col 46}{space 3}0.241{col 54}{space 4} -.161142{col 67}{space 3} .6422307
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0266304{col 26}{space 2} .1930702{col 37}{space 1}   -0.14{col 46}{space 3}0.890{col 54}{space 4}-.4050411{col 67}{space 3} .3517803
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0318597{col 26}{space 2} .1992475{col 37}{space 1}    0.16{col 46}{space 3}0.873{col 54}{space 4}-.3586582{col 67}{space 3} .4223776
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0574084{col 26}{space 2} .1942108{col 37}{space 1}   -0.30{col 46}{space 3}0.768{col 54}{space 4}-.4380546{col 67}{space 3} .3232377
{txt}health_frame {c |}{col 14}{res}{space 2} .5818696{col 26}{space 2} .1284209{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .3301692{col 67}{space 3}   .83357
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0664578{col 26}{space 2} .1540056{col 37}{space 1}    0.43{col 46}{space 3}0.666{col 54}{space 4}-.2353875{col 67}{space 3} .3683032
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2}-.0451525{col 26}{space 2}  .135827{col 37}{space 1}   -0.33{col 46}{space 3}0.740{col 54}{space 4}-.3113685{col 67}{space 3} .2210635
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7269364{col 26}{space 2} .1889096{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .3566803{col 67}{space 3} 1.097193
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-756.51927}  
Iteration 2:{space 3}log likelihood = {res:-756.33596}  
Iteration 3:{space 3}log likelihood = {res: -756.3359}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     31.28
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0010
{txt}Log likelihood = {res} -756.3359{txt}{col 49}Pseudo R2{col 67}= {res}    0.0203

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .3341928{col 28}{space 2} .2604791{col 39}{space 1}    1.28{col 48}{space 3}0.199{col 56}{space 4}-.1763367{col 69}{space 3} .8447224
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.0844268{col 28}{space 2} .2426369{col 39}{space 1}   -0.35{col 48}{space 3}0.728{col 56}{space 4}-.5599864{col 69}{space 3} .3911327
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .1675398{col 28}{space 2} .2530431{col 39}{space 1}    0.66{col 48}{space 3}0.508{col 56}{space 4}-.3284155{col 69}{space 3} .6634951
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2772201{col 28}{space 2} .2530561{col 39}{space 1}    1.10{col 48}{space 3}0.273{col 56}{space 4}-.2187607{col 69}{space 3} .7732009
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8457526{col 28}{space 2} .2921507{col 39}{space 1}    2.89{col 48}{space 3}0.004{col 56}{space 4} .2731477{col 69}{space 3} 1.418358
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0565324{col 28}{space 2} .1548288{col 39}{space 1}    0.37{col 48}{space 3}0.715{col 56}{space 4}-.2469264{col 69}{space 3} .3599912
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}-.0397128{col 28}{space 2} .1362743{col 39}{space 1}   -0.29{col 48}{space 3}0.771{col 56}{space 4}-.3068056{col 69}{space 3} .2273799
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2564932{col 28}{space 2} .4250507{col 39}{space 1}   -0.60{col 48}{space 3}0.546{col 56}{space 4}-1.089577{col 69}{space 3} .5765908
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1445599{col 28}{space 2} .4083642{col 39}{space 1}    0.35{col 48}{space 3}0.723{col 56}{space 4}-.6558192{col 69}{space 3} .9449389
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3610613{col 28}{space 2} .4124117{col 39}{space 1}   -0.88{col 48}{space 3}0.381{col 56}{space 4}-1.169373{col 69}{space 3} .4472507
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.8140939{col 28}{space 2} .3984644{col 39}{space 1}   -2.04{col 48}{space 3}0.041{col 56}{space 4} -1.59507{col 69}{space 3}-.0331181
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .6362411{col 28}{space 2} .2090349{col 39}{space 1}    3.04{col 48}{space 3}0.002{col 56}{space 4} .2265403{col 69}{space 3} 1.045942
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-753.33255}  
Iteration 2:{space 3}log likelihood = {res: -753.0989}  
Iteration 3:{space 3}log likelihood = {res:-753.09882}  
Iteration 4:{space 3}log likelihood = {res:-753.09882}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}     37.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0002
{txt}Log likelihood = {res}-753.09882{txt}{col 49}Pseudo R2{col 67}= {res}    0.0245

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .3265083{col 28}{space 2} .2613084{col 39}{space 1}    1.25{col 48}{space 3}0.211{col 56}{space 4}-.1856468{col 69}{space 3} .8386634
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1034393{col 28}{space 2} .2435811{col 39}{space 1}   -0.42{col 48}{space 3}0.671{col 56}{space 4}-.5808495{col 69}{space 3} .3739708
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .1328623{col 28}{space 2} .2541752{col 39}{space 1}    0.52{col 48}{space 3}0.601{col 56}{space 4}-.3653119{col 69}{space 3} .6310365
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2494766{col 28}{space 2} .2540932{col 39}{space 1}    0.98{col 48}{space 3}0.326{col 56}{space 4} -.248537{col 69}{space 3} .7474902
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8190895{col 28}{space 2} .2930073{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .2448056{col 69}{space 3} 1.393373
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0543483{col 28}{space 2} .1553156{col 39}{space 1}    0.35{col 48}{space 3}0.726{col 56}{space 4}-.2500647{col 69}{space 3} .3587613
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}-.0299314{col 28}{space 2} .1366867{col 39}{space 1}   -0.22{col 48}{space 3}0.827{col 56}{space 4}-.2978324{col 69}{space 3} .2379697
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2710122{col 28}{space 2} .4260439{col 39}{space 1}   -0.64{col 48}{space 3}0.525{col 56}{space 4}-1.106043{col 69}{space 3} .5640184
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1556641{col 28}{space 2} .4093546{col 39}{space 1}    0.38{col 48}{space 3}0.704{col 56}{space 4}-.6466561{col 69}{space 3} .9579844
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3291818{col 28}{space 2} .4135801{col 39}{space 1}   -0.80{col 48}{space 3}0.426{col 56}{space 4}-1.139784{col 69}{space 3} .4814203
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7990919{col 28}{space 2} .3995278{col 39}{space 1}   -2.00{col 48}{space 3}0.045{col 56}{space 4}-1.582152{col 69}{space 3}-.0160318
{txt}{space 9}white {c |}{col 16}{res}{space 2} .3784921{col 28}{space 2} .1470395{col 39}{space 1}    2.57{col 48}{space 3}0.010{col 56}{space 4} .0902999{col 69}{space 3} .6666843
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}   .36898{col 28}{space 2} .2334719{col 39}{space 1}    1.58{col 48}{space 3}0.114{col 56}{space 4}-.0886165{col 69}{space 3} .8265764
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-753.06954}  
Iteration 2:{space 3}log likelihood = {res:-752.83199}  
Iteration 3:{space 3}log likelihood = {res:-752.83191}  
Iteration 4:{space 3}log likelihood = {res:-752.83191}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}     38.29
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-752.83191{txt}{col 49}Pseudo R2{col 67}= {res}    0.0248

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .3213693{col 28}{space 2} .2614447{col 39}{space 1}    1.23{col 48}{space 3}0.219{col 56}{space 4}-.1910529{col 69}{space 3} .8337916
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1042931{col 28}{space 2} .2436448{col 39}{space 1}   -0.43{col 48}{space 3}0.669{col 56}{space 4}-.5818281{col 69}{space 3} .3732418
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .1311179{col 28}{space 2} .2542345{col 39}{space 1}    0.52{col 48}{space 3}0.606{col 56}{space 4}-.3671726{col 69}{space 3} .6294083
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2458613{col 28}{space 2} .2542018{col 39}{space 1}    0.97{col 48}{space 3}0.333{col 56}{space 4}-.2523651{col 69}{space 3} .7440876
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8202072{col 28}{space 2} .2930748{col 39}{space 1}    2.80{col 48}{space 3}0.005{col 56}{space 4} .2457911{col 69}{space 3} 1.394623
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0548252{col 28}{space 2} .1553317{col 39}{space 1}    0.35{col 48}{space 3}0.724{col 56}{space 4}-.2496194{col 69}{space 3} .3592697
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}-.0244837{col 28}{space 2} .1369409{col 39}{space 1}   -0.18{col 48}{space 3}0.858{col 56}{space 4}-.2928829{col 69}{space 3} .2439155
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2636589{col 28}{space 2} .4262115{col 39}{space 1}   -0.62{col 48}{space 3}0.536{col 56}{space 4}-1.099018{col 69}{space 3} .5717002
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1577949{col 28}{space 2}  .409444{col 39}{space 1}    0.39{col 48}{space 3}0.700{col 56}{space 4}-.6447005{col 69}{space 3} .9602903
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} -.326166{col 28}{space 2} .4137038{col 39}{space 1}   -0.79{col 48}{space 3}0.430{col 56}{space 4}-1.137011{col 69}{space 3} .4846786
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7935027{col 28}{space 2} .3996932{col 39}{space 1}   -1.99{col 48}{space 3}0.047{col 56}{space 4}-1.576887{col 69}{space 3}-.0101184
{txt}{space 9}white {c |}{col 16}{res}{space 2} .3692227{col 28}{space 2} .1475957{col 39}{space 1}    2.50{col 48}{space 3}0.012{col 56}{space 4} .0799404{col 69}{space 3}  .658505
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0330107{col 28}{space 2} .0451861{col 39}{space 1}    0.73{col 48}{space 3}0.465{col 56}{space 4}-.0555524{col 69}{space 3} .1215738
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .2757644{col 28}{space 2}  .265961{col 39}{space 1}    1.04{col 48}{space 3}0.300{col 56}{space 4}-.2455096{col 69}{space 3} .7970384
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-752.49455}  
Iteration 2:{space 3}log likelihood = {res:-752.24636}  
Iteration 3:{space 3}log likelihood = {res:-752.24627}  
Iteration 4:{space 3}log likelihood = {res:-752.24627}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}14{txt}){col 67}= {res}     39.46
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-752.24627{txt}{col 49}Pseudo R2{col 67}= {res}    0.0256

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}  .313649{col 28}{space 2} .2616575{col 39}{space 1}    1.20{col 48}{space 3}0.231{col 56}{space 4}-.1991902{col 69}{space 3} .8264882
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1168006{col 28}{space 2} .2440547{col 39}{space 1}   -0.48{col 48}{space 3}0.632{col 56}{space 4} -.595139{col 69}{space 3} .3615379
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .1284949{col 28}{space 2} .2543677{col 39}{space 1}    0.51{col 48}{space 3}0.613{col 56}{space 4}-.3700566{col 69}{space 3} .6270464
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2377566{col 28}{space 2} .2544266{col 39}{space 1}    0.93{col 48}{space 3}0.350{col 56}{space 4}-.2609103{col 69}{space 3} .7364236
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}  .804726{col 28}{space 2} .2935176{col 39}{space 1}    2.74{col 48}{space 3}0.006{col 56}{space 4} .2294421{col 69}{space 3}  1.38001
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0542381{col 28}{space 2} .1554274{col 39}{space 1}    0.35{col 48}{space 3}0.727{col 56}{space 4} -.250394{col 69}{space 3} .3588702
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} -.031041{col 28}{space 2} .1371321{col 39}{space 1}   -0.23{col 48}{space 3}0.821{col 56}{space 4} -.299815{col 69}{space 3} .2377329
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2583649{col 28}{space 2}  .426445{col 39}{space 1}   -0.61{col 48}{space 3}0.545{col 56}{space 4}-1.094182{col 69}{space 3} .5774518
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1744067{col 28}{space 2} .4099282{col 39}{space 1}    0.43{col 48}{space 3}0.671{col 56}{space 4}-.6290379{col 69}{space 3} .9778513
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3269462{col 28}{space 2} .4138898{col 39}{space 1}   -0.79{col 48}{space 3}0.430{col 56}{space 4}-1.138155{col 69}{space 3} .4842629
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7824985{col 28}{space 2} .3999947{col 39}{space 1}   -1.96{col 48}{space 3}0.050{col 56}{space 4}-1.566474{col 69}{space 3} .0014767
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4092179{col 28}{space 2} .1523338{col 39}{space 1}    2.69{col 48}{space 3}0.007{col 56}{space 4} .1106492{col 69}{space 3} .7077866
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0337931{col 28}{space 2} .0452061{col 39}{space 1}    0.75{col 48}{space 3}0.455{col 56}{space 4}-.0548093{col 69}{space 3} .1223955
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1504683{col 28}{space 2} .1386492{col 39}{space 1}   -1.09{col 48}{space 3}0.278{col 56}{space 4}-.4222158{col 69}{space 3} .1212792
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .3045747{col 28}{space 2} .2672775{col 39}{space 1}    1.14{col 48}{space 3}0.254{col 56}{space 4}-.2192796{col 69}{space 3} .8284289
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-747.87836}  
Iteration 2:{space 3}log likelihood = {res:-747.54607}  
Iteration 3:{space 3}log likelihood = {res:-747.54592}  
Iteration 4:{space 3}log likelihood = {res:-747.54592}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     48.86
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-747.54592{txt}{col 49}Pseudo R2{col 67}= {res}    0.0316

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .2905063{col 28}{space 2} .2627962{col 39}{space 1}    1.11{col 48}{space 3}0.269{col 56}{space 4}-.2245649{col 69}{space 3} .8055775
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1421176{col 28}{space 2} .2454464{col 39}{space 1}   -0.58{col 48}{space 3}0.563{col 56}{space 4}-.6231837{col 69}{space 3} .3389486
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}  .125323{col 28}{space 2} .2556535{col 39}{space 1}    0.49{col 48}{space 3}0.624{col 56}{space 4}-.3757486{col 69}{space 3} .6263947
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2164699{col 28}{space 2} .2555568{col 39}{space 1}    0.85{col 48}{space 3}0.397{col 56}{space 4}-.2844122{col 69}{space 3}  .717352
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8153762{col 28}{space 2} .2947246{col 39}{space 1}    2.77{col 48}{space 3}0.006{col 56}{space 4} .2377265{col 69}{space 3} 1.393026
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0216854{col 28}{space 2} .1563998{col 39}{space 1}    0.14{col 48}{space 3}0.890{col 56}{space 4}-.2848526{col 69}{space 3} .3282233
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}-.0009767{col 28}{space 2} .1380077{col 39}{space 1}   -0.01{col 48}{space 3}0.994{col 56}{space 4}-.2714669{col 69}{space 3} .2695134
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2804548{col 28}{space 2}  .427993{col 39}{space 1}   -0.66{col 48}{space 3}0.512{col 56}{space 4}-1.119306{col 69}{space 3} .5583959
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1762251{col 28}{space 2} .4113826{col 39}{space 1}    0.43{col 48}{space 3}0.668{col 56}{space 4}  -.63007{col 69}{space 3} .9825202
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3441635{col 28}{space 2} .4155691{col 39}{space 1}   -0.83{col 48}{space 3}0.408{col 56}{space 4}-1.158664{col 69}{space 3}  .470337
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7594689{col 28}{space 2} .4014655{col 39}{space 1}   -1.89{col 48}{space 3}0.059{col 56}{space 4}-1.546327{col 69}{space 3}  .027389
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4556241{col 28}{space 2} .1538387{col 39}{space 1}    2.96{col 48}{space 3}0.003{col 56}{space 4} .1541057{col 69}{space 3} .7571425
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0226917{col 28}{space 2} .0454999{col 39}{space 1}    0.50{col 48}{space 3}0.618{col 56}{space 4}-.0664864{col 69}{space 3} .1118698
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1329522{col 28}{space 2} .1392743{col 39}{space 1}   -0.95{col 48}{space 3}0.340{col 56}{space 4}-.4059247{col 69}{space 3} .1400203
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0078607{col 28}{space 2}  .002559{col 39}{space 1}    3.07{col 48}{space 3}0.002{col 56}{space 4} .0028451{col 69}{space 3} .0128762
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.2342066{col 28}{space 2} .3201314{col 39}{space 1}   -0.73{col 48}{space 3}0.464{col 56}{space 4}-.8616526{col 69}{space 3} .3932395
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-745.86186}  
Iteration 2:{space 3}log likelihood = {res:-745.48606}  
Iteration 3:{space 3}log likelihood = {res:-745.48587}  
Iteration 4:{space 3}log likelihood = {res:-745.48587}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     52.98
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-745.48587{txt}{col 49}Pseudo R2{col 67}= {res}    0.0343

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .3848887{col 28}{space 2} .6629339{col 39}{space 1}    0.58{col 48}{space 3}0.562{col 56}{space 4}-.9144379{col 69}{space 3} 1.684215
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.9145148{col 28}{space 2} .5921267{col 39}{space 1}   -1.54{col 48}{space 3}0.122{col 56}{space 4}-2.075062{col 69}{space 3} .2460322
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.6452122{col 28}{space 2} .5983279{col 39}{space 1}   -1.08{col 48}{space 3}0.281{col 56}{space 4}-1.817913{col 69}{space 3}  .527489
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.2956628{col 28}{space 2} .6044865{col 39}{space 1}   -0.49{col 48}{space 3}0.625{col 56}{space 4}-1.480434{col 69}{space 3}  .889109
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}  .807417{col 28}{space 2}  .293716{col 39}{space 1}    2.75{col 48}{space 3}0.006{col 56}{space 4} .2317443{col 69}{space 3}  1.38309
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0413375{col 28}{space 2} .1568473{col 39}{space 1}    0.26{col 48}{space 3}0.792{col 56}{space 4}-.2660776{col 69}{space 3} .3487526
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}  .005769{col 28}{space 2} .1386402{col 39}{space 1}    0.04{col 48}{space 3}0.967{col 56}{space 4}-.2659608{col 69}{space 3} .2774988
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2681252{col 28}{space 2}  .426816{col 39}{space 1}   -0.63{col 48}{space 3}0.530{col 56}{space 4}-1.104669{col 69}{space 3} .5684188
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .2044908{col 28}{space 2} .4124916{col 39}{space 1}    0.50{col 48}{space 3}0.620{col 56}{space 4}-.6039778{col 69}{space 3} 1.012959
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3406473{col 28}{space 2} .4161771{col 39}{space 1}   -0.82{col 48}{space 3}0.413{col 56}{space 4}-1.156339{col 69}{space 3} .4750448
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7416587{col 28}{space 2} .4020757{col 39}{space 1}   -1.84{col 48}{space 3}0.065{col 56}{space 4}-1.529713{col 69}{space 3} .0463953
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4550162{col 28}{space 2} .1542469{col 39}{space 1}    2.95{col 48}{space 3}0.003{col 56}{space 4} .1526978{col 69}{space 3} .7573345
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0257012{col 28}{space 2} .0456153{col 39}{space 1}    0.56{col 48}{space 3}0.573{col 56}{space 4}-.0637032{col 69}{space 3} .1151057
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1389592{col 28}{space 2} .1396737{col 39}{space 1}   -0.99{col 48}{space 3}0.320{col 56}{space 4}-.4127146{col 69}{space 3} .1347961
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2}  .001935{col 28}{space 2} .0053352{col 39}{space 1}    0.36{col 48}{space 3}0.717{col 56}{space 4}-.0085219{col 69}{space 3} .0123918
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2}-.0010377{col 28}{space 2}  .008452{col 39}{space 1}   -0.12{col 48}{space 3}0.902{col 56}{space 4}-.0176034{col 69}{space 3}  .015528
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0109105{col 28}{space 2} .0075961{col 39}{space 1}    1.44{col 48}{space 3}0.151{col 56}{space 4}-.0039777{col 69}{space 3} .0257986
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0111706{col 28}{space 2} .0078537{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-.0042223{col 69}{space 3} .0265636
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0073052{col 28}{space 2} .0077271{col 39}{space 1}    0.95{col 48}{space 3}0.344{col 56}{space 4}-.0078395{col 69}{space 3}   .02245
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .1535095{col 28}{space 2} .4494717{col 39}{space 1}    0.34{col 48}{space 3}0.733{col 56}{space 4}-.7274389{col 69}{space 3} 1.034458
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, mcmcsize(500000) burnin(50000) saving(simdata) rseed(32306): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   550,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    50,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   500,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .2825
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}    .00101
{col 65}{txt}avg ={col 71}{res}   .001553
{txt}Log marginal-likelihood = {res}-912.17003{col 65}{txt}max ={col 71}{res}   .004664
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .3192764{col 27}{space 2} .1609892{col 38}{space 2} .007051{col 48}{space 2} .3210229{col 59}{space 2} -.034053{col 70}{space 2} .6276254
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-.8587405{col 27}{space 2}  .209937{col 38}{space 2} .009239{col 48}{space 2}-.8737612{col 59}{space 2}-1.257942{col 70}{space 2}-.4498795
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.7123707{col 27}{space 2} .1012781{col 38}{space 2} .004338{col 48}{space 2} -.704006{col 59}{space 2}-.9076913{col 70}{space 2}-.5371244
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.2256912{col 27}{space 2} .1721879{col 38}{space 2} .007661{col 48}{space 2}-.2671187{col 59}{space 2} -.481189{col 70}{space 2} .1287896
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8192909{col 27}{space 2} .1192697{col 38}{space 2} .005241{col 48}{space 2} .8145585{col 59}{space 2} .5921827{col 70}{space 2} 1.056834
{txt}{space 7}shelter {c |}{col 16}{res}{space 1}-.2284909{col 27}{space 2} .0991939{col 38}{space 2} .004387{col 48}{space 2}-.2310868{col 59}{space 2}-.4296027{col 70}{space 2}-.0400902
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0918585{col 27}{space 2} .1132428{col 38}{space 2} .004546{col 48}{space 2} .0916859{col 59}{space 2}-.1278668{col 70}{space 2} .3075208
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.3079917{col 27}{space 2} .1356474{col 38}{space 2} .005877{col 48}{space 2}-.2969802{col 59}{space 2}-.6094201{col 70}{space 2}-.0810669
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .1791578{col 27}{space 2}  .194258{col 38}{space 2} .008477{col 48}{space 2} .1941595{col 59}{space 2}-.2383115{col 70}{space 2} .5134768
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3442549{col 27}{space 2} .1302793{col 38}{space 2} .005737{col 48}{space 2}-.3402273{col 59}{space 2} -.565623{col 70}{space 2}-.1121024
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.6280885{col 27}{space 2}  .227826{col 38}{space 2} .010044{col 48}{space 2} -.619133{col 59}{space 2}-1.078583{col 70}{space 2}-.1942117
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4711774{col 27}{space 2} .0985914{col 38}{space 2} .003867{col 48}{space 2} .4737368{col 59}{space 2} .2733176{col 70}{space 2} .6656194
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0227997{col 27}{space 2} .0436051{col 38}{space 2} .001413{col 48}{space 2} .0233766{col 59}{space 2}-.0621076{col 70}{space 2} .1074326
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.0991369{col 27}{space 2} .0942742{col 38}{space 2} .003247{col 48}{space 2}-.1045937{col 59}{space 2}-.2673628{col 70}{space 2} .1056199
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0030182{col 27}{space 2} .0031393{col 38}{space 2} .000095{col 48}{space 2} .0030229{col 59}{space 2}-.0031645{col 70}{space 2} .0090936
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0002051{col 27}{space 2} .0036146{col 38}{space 2} .000107{col 48}{space 2} .0001241{col 59}{space 2}-.0065941{col 70}{space 2} .0075795
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0105999{col 27}{space 2} .0039331{col 38}{space 2} .000127{col 48}{space 2} .0105501{col 59}{space 2} .0030278{col 70}{space 2} .0184832
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0123784{col 27}{space 2} .0030585{col 38}{space 2} .000063{col 48}{space 2} .0123553{col 59}{space 2} .0064678{col 70}{space 2} .0184877
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0060592{col 27}{space 2}   .00343{col 38}{space 2} .000099{col 48}{space 2} .0060799{col 59}{space 2} -.000678{col 70}{space 2} .0126837
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2332794{col 27}{space 2} .2263414{col 38}{space 2} .009643{col 48}{space 2} .2238367{col 59}{space 2}-.1876458{col 70}{space 2} .7124924
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. estimates store m1

. sum no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}      1,348    .7403561    .4386019          0          1
{txt}{space 7}cdc_m {c |}{res}      1,348    .1913947     .393545          0          1
{txt}{space 6}pres_m {c |}{res}      1,348    .2114243    .4084701          0          1
{txt}{space 5}state_m {c |}{res}      1,348    .1913947     .393545          0          1
{txt}{space 4}expert_m {c |}{res}      1,348    .2040059    .4031229          0          1
{txt}{hline 13}{c +}{hline 57}
health_frame {c |}{res}      1,348    .4651335     .498968          0          1
{txt}{space 5}shelter {c |}{res}      1,348    .7908012    .4068876          0          1
{txt}{space 5}jobloss {c |}{res}      1,348    .3048961    .4605343          0          1
{txt}{space 1}cdc_frame_h {c |}{res}      1,348    .0919881    .2891165          0          1
{txt}pres_frame_h {c |}{res}      1,348    .0994065    .2993181          0          1
{txt}{hline 13}{c +}{hline 57}
state_fram~h {c |}{res}      1,348    .0882789    .2838054          0          1
{txt}expert_fra~h {c |}{res}      1,348    .0949555    .2932622          0          1
{txt}{space 7}white {c |}{res}      1,348    .7863501    .4100345          0          1
{txt}{space 3}education {c |}{res}      1,348    2.999258    1.401823          1          5
{txt}{space 9}gop {c |}{res}      1,348    .3227003    .4676827          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}ideology_rs {c |}{res}      1,348    71.97494    24.68483          0        100
{txt}{space 3}cdc_ideol {c |}{res}      1,348    14.21035    30.92887          0        100
{txt}{space 2}pres_ideol {c |}{res}      1,348    15.43027    31.95092          0        100
{txt}{space 1}state_ideol {c |}{res}      1,348    13.69106    30.16558          0        100
{txt}expert_ideol {c |}{res}      1,348    14.51533    30.69289          0        100

{com}. bayes, prior({c -(}no_shop: ideology_rs white education{c )-}, normal(1,10)) prior({c -(}no_shop: cdc_ideol pres_ideol state_ideol expert_ideol{c )-}, normal(0,10)) mcmcsize(500000) burnin(50000) saving(simdata2) rseed(32306): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 60}{space 20}{res}{c -(}no_shop:ideology_rs white education{c )-}{txt} ~ normal(1,10){space 5}(1){p_end}
{p 0 60}{space 2}{res}{c -(}no_shop:cdc_ideol pres_ideol state_ideol expert_ideol{c )-}{txt} ~ normal(0,10){space 5}(1){p_end}
{p 0 60}{space 42}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 41}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:state_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 39}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 35}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:shelter{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 36}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 35}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 34}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 33}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 44}{res}{c -(}no_shop:gop{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 42}{res}{c -(}no_shop:_cons{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   550,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    50,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   500,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .2068
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .001482
{col 65}{txt}avg ={col 71}{res}   .003169
{txt}Log marginal-likelihood = {res}-886.84625{col 65}{txt}max ={col 71}{res}   .005895
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .0242741{col 27}{space 2} .3123357{col 38}{space 2} .010895{col 48}{space 2} .0335343{col 59}{space 2}-.6075975{col 70}{space 2} .6133105
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.119375{col 27}{space 2} .2253995{col 38}{space 2} .005771{col 48}{space 2}-1.118903{col 59}{space 2}-1.561541{col 70}{space 2}-.6795847
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-1.215179{col 27}{space 2} .3304022{col 38}{space 2} .011887{col 48}{space 2}-1.197003{col 59}{space 2}-1.915539{col 70}{space 2}-.6236417
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.5079381{col 27}{space 2} .3820925{col 38}{space 2} .014035{col 48}{space 2}-.4996957{col 59}{space 2}-1.282182{col 70}{space 2} .2155306
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8336611{col 27}{space 2} .2027044{col 38}{space 2} .004489{col 48}{space 2} .8342236{col 59}{space 2} .4379435{col 70}{space 2} 1.231064
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0652853{col 27}{space 2} .1566256{col 38}{space 2} .004054{col 48}{space 2} .0667561{col 59}{space 2}-.2437097{col 70}{space 2}  .368659
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1}  .025876{col 27}{space 2} .1370267{col 38}{space 2} .003275{col 48}{space 2} .0264469{col 59}{space 2}   -.2425{col 70}{space 2} .2935315
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.3501481{col 27}{space 2}  .233906{col 38}{space 2} .005494{col 48}{space 2}-.3519949{col 59}{space 2}-.8004313{col 70}{space 2}  .111783
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .1745745{col 27}{space 2} .2781011{col 38}{space 2} .006291{col 48}{space 2} .1733764{col 59}{space 2}-.3642467{col 70}{space 2} .7293249
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3027375{col 27}{space 2} .3581886{col 38}{space 2}  .00749{col 48}{space 2}-.3055693{col 59}{space 2}-.9963024{col 70}{space 2} .4117601
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7609267{col 27}{space 2} .3104332{col 38}{space 2} .006391{col 48}{space 2}-.7637151{col 59}{space 2} -1.36634{col 70}{space 2} -.146736
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4751557{col 27}{space 2} .1411122{col 38}{space 2} .004821{col 48}{space 2} .4776731{col 59}{space 2} .1898605{col 70}{space 2} .7442808
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0359216{col 27}{space 2} .0426794{col 38}{space 2} .000786{col 48}{space 2} .0358632{col 59}{space 2}-.0473379{col 70}{space 2} .1201637
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1415897{col 27}{space 2} .1214532{col 38}{space 2} .002328{col 48}{space 2}-.1413898{col 59}{space 2}-.3784639{col 70}{space 2} .0962123
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1}-.0006634{col 27}{space 2} .0029355{col 38}{space 2} .000065{col 48}{space 2}-.0006578{col 59}{space 2}-.0064369{col 70}{space 2} .0051245
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0038801{col 27}{space 2} .0048288{col 38}{space 2} .000143{col 48}{space 2} .0038244{col 59}{space 2}-.0053329{col 70}{space 2} .0134913
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0135341{col 27}{space 2} .0042675{col 38}{space 2} .000112{col 48}{space 2} .0135707{col 59}{space 2} .0051455{col 70}{space 2} .0217866
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0183775{col 27}{space 2} .0048513{col 38}{space 2} .000151{col 48}{space 2} .0182061{col 59}{space 2}  .009341{col 70}{space 2} .0283922
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0100961{col 27}{space 2} .0054447{col 38}{space 2} .000181{col 48}{space 2} .0100062{col 59}{space 2}-.0003094{col 70}{space 2} .0211075
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2966102{col 27}{space 2} .1884636{col 38}{space 2} .005925{col 48}{space 2} .2968265{col 59}{space 2}-.0692352{col 70}{space 2} .6644917
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for some model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. estimates store m2

. bayesstats ess
{res}
{txt}Efficiency summaries{col 27}MCMC sample size{col 44}={col 45}{res}   500,000
{col 27}{txt}Efficiency:{col 40}min ={col 45}{res}   .001482
{col 40}{txt}avg ={col 45}{res}   .003169
{txt}{col 40}max ={col 45}{res}   .005895
 
{hline 15}{col 16}{c TT}{hline 38}
{col 7}{txt}no_shop{col 16}{c |}{col 25}ESS{col 31}Corr. time{col 45}Efficiency
{res}{txt}{hline 15}{c +}{hline 11}{hline 13}{hline 14}
{space 9}cdc_m {c |}{col 16}{res}{space 1}    821.78{col 28}{space 2}     608.43{col 41}{space 2}      0.0016
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}   1525.21{col 28}{space 2}     327.82{col 41}{space 2}      0.0031
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}    772.53{col 28}{space 2}     647.22{col 41}{space 2}      0.0015
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}    741.13{col 28}{space 2}     674.65{col 41}{space 2}      0.0015
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1}   2038.85{col 28}{space 2}     245.24{col 41}{space 2}      0.0041
{txt}{space 7}shelter {c |}{col 16}{res}{space 1}   1492.85{col 28}{space 2}     334.93{col 41}{space 2}      0.0030
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1}   1750.33{col 28}{space 2}     285.66{col 41}{space 2}      0.0035
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}   1812.36{col 28}{space 2}     275.88{col 41}{space 2}      0.0036
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1}   1954.05{col 28}{space 2}     255.88{col 41}{space 2}      0.0039
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}   2286.95{col 28}{space 2}     218.63{col 41}{space 2}      0.0046
{txt}expert_frame_h {c |}{col 16}{res}{space 1}   2359.25{col 28}{space 2}     211.93{col 41}{space 2}      0.0047
{txt}{space 9}white {c |}{col 16}{res}{space 1}    856.71{col 28}{space 2}     583.62{col 41}{space 2}      0.0017
{txt}{space 5}education {c |}{col 16}{res}{space 1}   2947.40{col 28}{space 2}     169.64{col 41}{space 2}      0.0059
{txt}{space 11}gop {c |}{col 16}{res}{space 1}   2721.36{col 28}{space 2}     183.73{col 41}{space 2}      0.0054
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1}   2067.50{col 28}{space 2}     241.84{col 41}{space 2}      0.0041
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1}   1141.80{col 28}{space 2}     437.90{col 41}{space 2}      0.0023
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1}   1454.85{col 28}{space 2}     343.68{col 41}{space 2}      0.0029
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1}   1030.15{col 28}{space 2}     485.37{col 41}{space 2}      0.0021
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}    900.43{col 28}{space 2}     555.29{col 41}{space 2}      0.0018
{txt}{space 9}_cons {c |}{col 16}{res}{space 1}   1011.80{col 28}{space 2}     494.17{col 41}{space 2}      0.0020
{txt}{hline 15}{c BT}{hline 11}{hline 13}{hline 14}

{com}. bayesgraph diagnostic {c -(}{c )-}
{res}{err}invalid specification {bf:}}
{txt}{search r(198), local:r(198);}

{com}. bayesgraph diagnostic
{res}{err}you must specify at least one parameter
{txt}{search r(111), local:r(111);}

{com}. bayesgraph diagnostic {c -(}cdc_m{c )-}
{res}
{com}. bayesgraph diagnostic {c -(}health_frame{c )-}
{res}
{com}. bayesgraph diagnostic {c -(} pres_m{c )-}
{res}
{com}. bayesstats ic m2 m1, bayesfactor
{res}
{txt}Bayesian information criteria

{res}{txt}{hline 13}{c TT}{hline 10}{hline 11}{hline 11}
{col 14}{c |}       DIC{col 25}    log(ML){col 36}         BF
{hline 13}{c +}{hline 10}{hline 11}{hline 11}
{space 10}m2 {c |}{col 14}{res}{space 1} 1522.653{col 25}{space 2}-886.8463{col 36}{space 2}        .
{txt}{space 10}m1 {c |}{col 14}{res}{space 1} 1517.414{col 25}{space 2}  -912.17{col 36}{space 2} 1.00e-11
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 11}
{p 0 6 0 46}Note: Marginal likelihood (ML) is computed using Laplace-Metropolis approximation.{p_end}

{com}. bayestest model m1 m2
{res}
{txt}Bayesian model tests

{res}{txt}{hline 13}{c TT}{hline 10}{hline 11}{hline 11}
{col 14}{c |}   log(ML){col 25}       P(M){col 36}     P(M|y)
{hline 13}{c +}{hline 10}{hline 11}{hline 11}
{space 10}m1 {c |}{col 14}{res}{space 1}-912.1700{col 25}{space 2}   0.5000{col 36}{space 2}   0.0000
{txt}{space 10}m2 {c |}{col 14}{res}{space 1}-886.8463{col 25}{space 2}   0.5000{col 36}{space 2}   1.0000
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 11}
{p 0 6 0 48}Note: Marginal likelihood (ML)
is computed using Laplace-Metropolis approximation.{p_end}

{com}. erase simdata.dta

. erase simdata2.dta

. estimates drop m1 m2
{res}
{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata) rseed(32306): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ normal(0,10000){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4483
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .002221
{col 65}{txt}avg ={col 71}{res}    .02122
{txt}Log marginal-likelihood = {res}-891.38645{col 65}{txt}max ={col 71}{res}     .1141
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .3242405{col 27}{space 2} .6733577{col 38}{space 2} .037706{col 48}{space 2} .3316997{col 59}{space 2}-1.004419{col 70}{space 2} 1.645472
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.025681{col 27}{space 2} .5973639{col 38}{space 2} .032407{col 48}{space 2}-1.026452{col 59}{space 2}-2.194929{col 70}{space 2} .1554181
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.7059342{col 27}{space 2} .6092261{col 38}{space 2}  .03315{col 48}{space 2}-.6964368{col 59}{space 2}-1.907847{col 70}{space 2} .4916401
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.3660341{col 27}{space 2} .6064625{col 38}{space 2} .034008{col 48}{space 2}-.3573445{col 59}{space 2}-1.601761{col 70}{space 2} .8020965
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8356583{col 27}{space 2}  .293725{col 38}{space 2} .009017{col 48}{space 2} .8310162{col 59}{space 2} .2732845{col 70}{space 2} 1.417136
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0415236{col 27}{space 2} .1604344{col 38}{space 2} .002862{col 48}{space 2} .0419149{col 59}{space 2}-.2714339{col 70}{space 2} .3531327
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0077968{col 27}{space 2} .1391005{col 38}{space 2} .001336{col 48}{space 2} .0070085{col 59}{space 2}-.2629906{col 70}{space 2} .2804768
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2792028{col 27}{space 2} .4283904{col 38}{space 2} .010739{col 48}{space 2}-.2814103{col 59}{space 2}-1.113204{col 70}{space 2}  .558904
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .1992678{col 27}{space 2}  .414085{col 38}{space 2}  .00969{col 48}{space 2} .1981116{col 59}{space 2}-.6086688{col 70}{space 2} 1.014537
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3584254{col 27}{space 2}  .420953{col 38}{space 2} .009605{col 48}{space 2}-.3523072{col 59}{space 2}-1.190159{col 70}{space 2} .4576576
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7697026{col 27}{space 2} .4023809{col 38}{space 2} .010277{col 48}{space 2}-.7697623{col 59}{space 2}-1.556782{col 70}{space 2} .0183881
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4606011{col 27}{space 2} .1566128{col 38}{space 2} .002748{col 48}{space 2} .4599994{col 59}{space 2} .1570766{col 70}{space 2} .7678238
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0265163{col 27}{space 2} .0462921{col 38}{space 2} .000883{col 48}{space 2}  .026453{col 59}{space 2}-.0651051{col 70}{space 2} .1163334
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1399108{col 27}{space 2} .1418392{col 38}{space 2} .001328{col 48}{space 2} -.139319{col 59}{space 2}-.4166207{col 70}{space 2} .1375627
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0010707{col 27}{space 2} .0053262{col 38}{space 2} .000357{col 48}{space 2} .0011156{col 59}{space 2} -.009909{col 70}{space 2} .0110066
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1}-.0001004{col 27}{space 2} .0085939{col 38}{space 2} .000473{col 48}{space 2} -.000206{col 59}{space 2}-.0165582{col 70}{space 2} .0172097
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0124273{col 27}{space 2} .0076345{col 38}{space 2} .000416{col 48}{space 2} .0124727{col 59}{space 2}-.0026671{col 70}{space 2} .0276609
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0120901{col 27}{space 2} .0079858{col 38}{space 2} .000426{col 48}{space 2} .0119943{col 59}{space 2} -.003619{col 70}{space 2} .0276755
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0083708{col 27}{space 2} .0077928{col 38}{space 2} .000434{col 48}{space 2} .0082824{col 59}{space 2}-.0066269{col 70}{space 2} .0242609
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2178466{col 27}{space 2} .4520542{col 38}{space 2} .029239{col 48}{space 2} .2141037{col 59}{space 2}-.6381015{col 70}{space 2} 1.158584
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. estimates store m1

. bayesgraph diagnostic {c -(}cdc_m{c )-}
{res}
{com}. bayesgraph diagnostic {c -(} pres_m{c )-}
{res}
{com}. bayesgraph diagnostic {c -(}health_frame{c )-}
{res}
{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-771.97822}  
Iteration 1:{space 3}log likelihood = {res:-745.86186}  
Iteration 2:{space 3}log likelihood = {res:-745.48606}  
Iteration 3:{space 3}log likelihood = {res:-745.48587}  
Iteration 4:{space 3}log likelihood = {res:-745.48587}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,348
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     52.98
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-745.48587{txt}{col 49}Pseudo R2{col 67}= {res}    0.0343

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .3848887{col 28}{space 2} .6629339{col 39}{space 1}    0.58{col 48}{space 3}0.562{col 56}{space 4}-.9144379{col 69}{space 3} 1.684215
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.9145148{col 28}{space 2} .5921267{col 39}{space 1}   -1.54{col 48}{space 3}0.122{col 56}{space 4}-2.075062{col 69}{space 3} .2460322
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.6452122{col 28}{space 2} .5983279{col 39}{space 1}   -1.08{col 48}{space 3}0.281{col 56}{space 4}-1.817913{col 69}{space 3}  .527489
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.2956628{col 28}{space 2} .6044865{col 39}{space 1}   -0.49{col 48}{space 3}0.625{col 56}{space 4}-1.480434{col 69}{space 3}  .889109
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}  .807417{col 28}{space 2}  .293716{col 39}{space 1}    2.75{col 48}{space 3}0.006{col 56}{space 4} .2317443{col 69}{space 3}  1.38309
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0413375{col 28}{space 2} .1568473{col 39}{space 1}    0.26{col 48}{space 3}0.792{col 56}{space 4}-.2660776{col 69}{space 3} .3487526
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}  .005769{col 28}{space 2} .1386402{col 39}{space 1}    0.04{col 48}{space 3}0.967{col 56}{space 4}-.2659608{col 69}{space 3} .2774988
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2681252{col 28}{space 2}  .426816{col 39}{space 1}   -0.63{col 48}{space 3}0.530{col 56}{space 4}-1.104669{col 69}{space 3} .5684188
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .2044908{col 28}{space 2} .4124916{col 39}{space 1}    0.50{col 48}{space 3}0.620{col 56}{space 4}-.6039778{col 69}{space 3} 1.012959
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3406473{col 28}{space 2} .4161771{col 39}{space 1}   -0.82{col 48}{space 3}0.413{col 56}{space 4}-1.156339{col 69}{space 3} .4750448
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7416587{col 28}{space 2} .4020757{col 39}{space 1}   -1.84{col 48}{space 3}0.065{col 56}{space 4}-1.529713{col 69}{space 3} .0463953
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4550162{col 28}{space 2} .1542469{col 39}{space 1}    2.95{col 48}{space 3}0.003{col 56}{space 4} .1526978{col 69}{space 3} .7573345
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0257012{col 28}{space 2} .0456153{col 39}{space 1}    0.56{col 48}{space 3}0.573{col 56}{space 4}-.0637032{col 69}{space 3} .1151057
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1389592{col 28}{space 2} .1396737{col 39}{space 1}   -0.99{col 48}{space 3}0.320{col 56}{space 4}-.4127146{col 69}{space 3} .1347961
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2}  .001935{col 28}{space 2} .0053352{col 39}{space 1}    0.36{col 48}{space 3}0.717{col 56}{space 4}-.0085219{col 69}{space 3} .0123918
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2}-.0010377{col 28}{space 2}  .008452{col 39}{space 1}   -0.12{col 48}{space 3}0.902{col 56}{space 4}-.0176034{col 69}{space 3}  .015528
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0109105{col 28}{space 2} .0075961{col 39}{space 1}    1.44{col 48}{space 3}0.151{col 56}{space 4}-.0039777{col 69}{space 3} .0257986
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0111706{col 28}{space 2} .0078537{col 39}{space 1}    1.42{col 48}{space 3}0.155{col 56}{space 4}-.0042223{col 69}{space 3} .0265636
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0073052{col 28}{space 2} .0077271{col 39}{space 1}    0.95{col 48}{space 3}0.344{col 56}{space 4}-.0078395{col 69}{space 3}   .02245
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .1535095{col 28}{space 2} .4494717{col 39}{space 1}    0.34{col 48}{space 3}0.733{col 56}{space 4}-.7274389{col 69}{space 3} 1.034458
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4474
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .002434
{col 65}{txt}avg ={col 71}{res}    .02074
{txt}Log marginal-likelihood = {res}-841.05008{col 65}{txt}max ={col 71}{res}     .1105
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .2831857{col 27}{space 2} .6430288{col 38}{space 2} .031656{col 48}{space 2} .2779978{col 59}{space 2}-.9659911{col 70}{space 2} 1.546595
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.043622{col 27}{space 2} .5722402{col 38}{space 2} .028667{col 48}{space 2}-1.046234{col 59}{space 2}  -2.1435{col 70}{space 2} .0947145
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.7537072{col 27}{space 2} .5939124{col 38}{space 2} .031492{col 48}{space 2}-.7551286{col 59}{space 2}-1.906793{col 70}{space 2} .4354907
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.3953186{col 27}{space 2} .5990663{col 38}{space 2} .031835{col 48}{space 2}-.4040052{col 59}{space 2}-1.569546{col 70}{space 2} .7992103
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8199422{col 27}{space 2} .2955073{col 38}{space 2} .009059{col 48}{space 2} .8205362{col 59}{space 2} .2452172{col 70}{space 2} 1.398247
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0404143{col 27}{space 2} .1602751{col 38}{space 2} .002798{col 48}{space 2} .0401811{col 59}{space 2}-.2751674{col 70}{space 2} .3505788
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0060619{col 27}{space 2}  .139951{col 38}{space 2} .001384{col 48}{space 2}  .005344{col 59}{space 2} -.267529{col 70}{space 2} .2813095
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2619147{col 27}{space 2}  .427937{col 38}{space 2} .010389{col 48}{space 2}-.2686857{col 59}{space 2}-1.083762{col 70}{space 2} .5898811
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .2146161{col 27}{space 2} .4169351{col 38}{space 2} .010226{col 48}{space 2} .2135961{col 59}{space 2}-.5966479{col 70}{space 2}  1.03871
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3428776{col 27}{space 2}  .419886{col 38}{space 2} .010694{col 48}{space 2}-.3408507{col 59}{space 2}-1.165187{col 70}{space 2} .4642733
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7511599{col 27}{space 2} .4066276{col 38}{space 2} .011006{col 48}{space 2}-.7540468{col 59}{space 2}-1.540584{col 70}{space 2} .0572072
{txt}{space 9}white {c |}{col 16}{res}{space 1}  .458307{col 27}{space 2} .1574497{col 38}{space 2} .002782{col 48}{space 2} .4576138{col 59}{space 2} .1483474{col 70}{space 2}  .765743
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0256864{col 27}{space 2} .0457545{col 38}{space 2} .000862{col 48}{space 2} .0258528{col 59}{space 2} -.064209{col 70}{space 2}  .115115
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1414013{col 27}{space 2} .1411476{col 38}{space 2} .001343{col 48}{space 2} -.141107{col 59}{space 2}-.4178767{col 70}{space 2} .1348183
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0007763{col 27}{space 2} .0051757{col 38}{space 2} .000332{col 48}{space 2} .0008039{col 59}{space 2}-.0092649{col 70}{space 2} .0109156
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0003336{col 27}{space 2} .0081698{col 38}{space 2} .000397{col 48}{space 2} .0003293{col 59}{space 2}-.0155378{col 70}{space 2} .0164999
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0125765{col 27}{space 2} .0073023{col 38}{space 2} .000366{col 48}{space 2} .0125588{col 59}{space 2}-.0019417{col 70}{space 2} .0266675
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0126454{col 27}{space 2} .0077527{col 38}{space 2} .000403{col 48}{space 2} .0126323{col 59}{space 2}-.0024616{col 70}{space 2} .0277134
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}  .008659{col 27}{space 2} .0077178{col 38}{space 2} .000406{col 48}{space 2}  .008736{col 59}{space 2}-.0066271{col 70}{space 2} .0237316
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2531256{col 27}{space 2} .4325913{col 38}{space 2} .027123{col 48}{space 2} .2512392{col 59}{space 2}-.5903438{col 70}{space 2} 1.114707
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. estimates store m2

. bayesgraph diagnostic {c -(}cdc_m{c )-}
{res}
{com}. bayesgraph diagnostic {c -(}health_frame{c )-}
{res}
{com}. bayesstats ic m2 m1, bayesfactor
{res}
{txt}Bayesian information criteria

{res}{txt}{hline 13}{c TT}{hline 10}{hline 11}{hline 11}
{col 14}{c |}       DIC{col 25}    log(ML){col 36}         BF
{hline 13}{c +}{hline 10}{hline 11}{hline 11}
{space 10}m2 {c |}{col 14}{res}{space 1} 1531.157{col 25}{space 2}-841.0501{col 36}{space 2}        .
{txt}{space 10}m1 {c |}{col 14}{res}{space 1} 1531.292{col 25}{space 2}-891.3865{col 36}{space 2} 1.38e-22
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 11}
{p 0 6 0 46}Note: Marginal likelihood (ML) is computed using Laplace-Metropolis approximation.{p_end}

{com}. bayestest model m1 m2
{res}
{txt}Bayesian model tests

{res}{txt}{hline 13}{c TT}{hline 10}{hline 11}{hline 11}
{col 14}{c |}   log(ML){col 25}       P(M){col 36}     P(M|y)
{hline 13}{c +}{hline 10}{hline 11}{hline 11}
{space 10}m1 {c |}{col 14}{res}{space 1}-891.3865{col 25}{space 2}   0.5000{col 36}{space 2}   0.0000
{txt}{space 10}m2 {c |}{col 14}{res}{space 1}-841.0501{col 25}{space 2}   0.5000{col 36}{space 2}   1.0000
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 11}
{p 0 6 0 48}Note: Marginal likelihood (ML)
is computed using Laplace-Metropolis approximation.{p_end}

{com}. bayes, bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) prior({c -(}no_shop: ideology_rs white education{c )-}, normal(1,10)) prior({c -(}no_shop: cdc_ideol pres_ideol state_ideol expert_ideol{c )-}, normal(0,10)) saving(simdata3) rseed(32306): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}{err}invalid 'block' 
{txt}{search r(198), local:r(198);}

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) prior({c -(}no_shop: ideology_rs white education{c )-}, normal(1,10)) prior({c -(}no_shop: cdc_ideol pres_ideol state_ideol expert_ideol{c )-}, normal(0,10)) saving(simdata3) rseed(32306): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata3.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 60}{space 20}{res}{c -(}no_shop:ideology_rs white education{c )-}{txt} ~ normal(1,10){space 5}(1){p_end}
{p 0 60}{space 2}{res}{c -(}no_shop:cdc_ideol pres_ideol state_ideol expert_ideol{c )-}{txt} ~ normal(0,10){space 5}(1){p_end}
{p 0 60}{space 42}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 41}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:state_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 39}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 35}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:shelter{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 40}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 36}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 35}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 34}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 33}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 44}{res}{c -(}no_shop:gop{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{p 0 60}{space 42}{res}{c -(}no_shop:_cons{c )-}{txt} ~ normal(0,10000){space 2}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4494
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .001933
{col 65}{txt}avg ={col 71}{res}    .02057
{txt}Log marginal-likelihood = {res}-867.58211{col 65}{txt}max ={col 71}{res}     .1028
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .3258728{col 27}{space 2} .6750783{col 38}{space 2} .039499{col 48}{space 2} .3240631{col 59}{space 2}-.9631988{col 70}{space 2} 1.649475
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}   -1.031{col 27}{space 2} .6041704{col 38}{space 2} .035453{col 48}{space 2}-1.020652{col 59}{space 2} -2.24065{col 70}{space 2}  .123431
{txt}{space 7}state_m {c |}{col 16}{res}{space 1} -.732504{col 27}{space 2} .6261533{col 38}{space 2} .036951{col 48}{space 2}-.7436019{col 59}{space 2}-1.974204{col 70}{space 2} .5057417
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.3627095{col 27}{space 2} .6324257{col 38}{space 2} .037428{col 48}{space 2}-.3557001{col 59}{space 2}-1.638563{col 70}{space 2} .8661945
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8211737{col 27}{space 2} .2930568{col 38}{space 2}  .00836{col 48}{space 2} .8208978{col 59}{space 2} .2492013{col 70}{space 2} 1.403969
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0403292{col 27}{space 2} .1611411{col 38}{space 2} .002908{col 48}{space 2}  .039306{col 59}{space 2}-.2760251{col 70}{space 2} .3547828
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0060192{col 27}{space 2}  .139386{col 38}{space 2} .001375{col 48}{space 2} .0044934{col 59}{space 2} -.263496{col 70}{space 2} .2814219
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2661325{col 27}{space 2} .4309122{col 38}{space 2} .009612{col 48}{space 2}-.2692813{col 59}{space 2}-1.098535{col 70}{space 2} .5895914
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .2144464{col 27}{space 2} .4129695{col 38}{space 2} .009499{col 48}{space 2} .2115789{col 59}{space 2}-.5875718{col 70}{space 2} 1.027825
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3423168{col 27}{space 2} .4175162{col 38}{space 2} .009385{col 48}{space 2}-.3422207{col 59}{space 2}-1.163418{col 70}{space 2} .4713505
{txt}expert_frame_h {c |}{col 16}{res}{space 1}  -.75536{col 27}{space 2} .4028617{col 38}{space 2} .009819{col 48}{space 2}-.7604184{col 59}{space 2}-1.533966{col 70}{space 2} .0502635
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4616721{col 27}{space 2} .1556069{col 38}{space 2} .002767{col 48}{space 2} .4614931{col 59}{space 2} .1545673{col 70}{space 2} .7697392
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0255773{col 27}{space 2} .0462497{col 38}{space 2} .000865{col 48}{space 2} .0254148{col 59}{space 2}-.0644021{col 70}{space 2} .1166558
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1423902{col 27}{space 2} .1415442{col 38}{space 2} .001397{col 48}{space 2}-.1425604{col 59}{space 2}-.4213204{col 70}{space 2} .1371903
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0010233{col 27}{space 2} .0057373{col 38}{space 2} .000413{col 48}{space 2} .0010883{col 59}{space 2}-.0101116{col 70}{space 2}   .01194
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1}-.0001968{col 27}{space 2} .0086731{col 38}{space 2}  .00051{col 48}{space 2}-.0002384{col 59}{space 2}-.0174168{col 70}{space 2} .0166983
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0123929{col 27}{space 2} .0078451{col 38}{space 2} .000465{col 48}{space 2} .0121962{col 59}{space 2}-.0025784{col 70}{space 2} .0281094
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0123452{col 27}{space 2} .0082775{col 38}{space 2} .000484{col 48}{space 2} .0124563{col 59}{space 2}-.0039155{col 70}{space 2} .0286999
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}  .008239{col 27}{space 2} .0082429{col 38}{space 2} .000484{col 48}{space 2} .0081674{col 59}{space 2}-.0079324{col 70}{space 2} .0246309
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2330232{col 27}{space 2} .4805967{col 38}{space 2} .033771{col 48}{space 2} .2120935{col 59}{space 2}-.6780153{col 70}{space 2} 1.217455
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for some model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. estimates store m3

. bayesgraph diagnostic {c -(}cdc_m{c )-}
{res}
{com}. bayesgraph diagnostic {c -(}health_frame{c )-}
{res}
{com}. bayesstats ic m3 m2 m1, bayesfactor
{res}
{txt}Bayesian information criteria

{res}{txt}{hline 13}{c TT}{hline 10}{hline 11}{hline 11}
{col 14}{c |}       DIC{col 25}    log(ML){col 36}         BF
{hline 13}{c +}{hline 10}{hline 11}{hline 11}
{space 10}m3 {c |}{col 14}{res}{space 1} 1531.734{col 25}{space 2}-867.5821{col 36}{space 2}        .
{txt}{space 10}m2 {c |}{col 14}{res}{space 1} 1531.157{col 25}{space 2}-841.0501{col 36}{space 2} 3.33e+11
{txt}{space 10}m1 {c |}{col 14}{res}{space 1} 1531.292{col 25}{space 2}-891.3865{col 36}{space 2} 4.59e-11
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 11}
{p 0 6 0 46}Note: Marginal likelihood (ML) is computed using Laplace-Metropolis approximation.{p_end}

{com}. erase simdata.dta

. erase simdata2.dta

. erase simdata3.dta

. estimates drop m1 m2 m3
{res}
{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4474
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .002434
{col 65}{txt}avg ={col 71}{res}    .02074
{txt}Log marginal-likelihood = {res}-841.05008{col 65}{txt}max ={col 71}{res}     .1105
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .2831857{col 27}{space 2} .6430288{col 38}{space 2} .031656{col 48}{space 2} .2779978{col 59}{space 2}-.9659911{col 70}{space 2} 1.546595
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.043622{col 27}{space 2} .5722402{col 38}{space 2} .028667{col 48}{space 2}-1.046234{col 59}{space 2}  -2.1435{col 70}{space 2} .0947145
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.7537072{col 27}{space 2} .5939124{col 38}{space 2} .031492{col 48}{space 2}-.7551286{col 59}{space 2}-1.906793{col 70}{space 2} .4354907
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.3953186{col 27}{space 2} .5990663{col 38}{space 2} .031835{col 48}{space 2}-.4040052{col 59}{space 2}-1.569546{col 70}{space 2} .7992103
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8199422{col 27}{space 2} .2955073{col 38}{space 2} .009059{col 48}{space 2} .8205362{col 59}{space 2} .2452172{col 70}{space 2} 1.398247
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0404143{col 27}{space 2} .1602751{col 38}{space 2} .002798{col 48}{space 2} .0401811{col 59}{space 2}-.2751674{col 70}{space 2} .3505788
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0060619{col 27}{space 2}  .139951{col 38}{space 2} .001384{col 48}{space 2}  .005344{col 59}{space 2} -.267529{col 70}{space 2} .2813095
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2619147{col 27}{space 2}  .427937{col 38}{space 2} .010389{col 48}{space 2}-.2686857{col 59}{space 2}-1.083762{col 70}{space 2} .5898811
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .2146161{col 27}{space 2} .4169351{col 38}{space 2} .010226{col 48}{space 2} .2135961{col 59}{space 2}-.5966479{col 70}{space 2}  1.03871
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3428776{col 27}{space 2}  .419886{col 38}{space 2} .010694{col 48}{space 2}-.3408507{col 59}{space 2}-1.165187{col 70}{space 2} .4642733
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7511599{col 27}{space 2} .4066276{col 38}{space 2} .011006{col 48}{space 2}-.7540468{col 59}{space 2}-1.540584{col 70}{space 2} .0572072
{txt}{space 9}white {c |}{col 16}{res}{space 1}  .458307{col 27}{space 2} .1574497{col 38}{space 2} .002782{col 48}{space 2} .4576138{col 59}{space 2} .1483474{col 70}{space 2}  .765743
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0256864{col 27}{space 2} .0457545{col 38}{space 2} .000862{col 48}{space 2} .0258528{col 59}{space 2} -.064209{col 70}{space 2}  .115115
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1414013{col 27}{space 2} .1411476{col 38}{space 2} .001343{col 48}{space 2} -.141107{col 59}{space 2}-.4178767{col 70}{space 2} .1348183
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0007763{col 27}{space 2} .0051757{col 38}{space 2} .000332{col 48}{space 2} .0008039{col 59}{space 2}-.0092649{col 70}{space 2} .0109156
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0003336{col 27}{space 2} .0081698{col 38}{space 2} .000397{col 48}{space 2} .0003293{col 59}{space 2}-.0155378{col 70}{space 2} .0164999
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0125765{col 27}{space 2} .0073023{col 38}{space 2} .000366{col 48}{space 2} .0125588{col 59}{space 2}-.0019417{col 70}{space 2} .0266675
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0126454{col 27}{space 2} .0077527{col 38}{space 2} .000403{col 48}{space 2} .0126323{col 59}{space 2}-.0024616{col 70}{space 2} .0277134
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}  .008659{col 27}{space 2} .0077178{col 38}{space 2} .000406{col 48}{space 2}  .008736{col 59}{space 2}-.0066271{col 70}{space 2} .0237316
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2531256{col 27}{space 2} .4325913{col 38}{space 2} .027123{col 48}{space 2} .2512392{col 59}{space 2}-.5903438{col 70}{space 2} 1.114707
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayesstats ess
{res}
{txt}Efficiency summaries{col 27}MCMC sample size{col 44}={col 45}{res}   100,000
{col 27}{txt}Efficiency:{col 40}min ={col 45}{res}   .002434
{col 40}{txt}avg ={col 45}{res}    .02074
{txt}{col 40}max ={col 45}{res}     .1105
 
{hline 15}{col 16}{c TT}{hline 38}
{col 7}{txt}no_shop{col 16}{c |}{col 25}ESS{col 31}Corr. time{col 45}Efficiency
{res}{txt}{hline 15}{c +}{hline 11}{hline 13}{hline 14}
{space 9}cdc_m {c |}{col 16}{res}{space 1}    412.62{col 28}{space 2}     242.35{col 41}{space 2}      0.0041
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}    398.47{col 28}{space 2}     250.96{col 41}{space 2}      0.0040
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}    355.67{col 28}{space 2}     281.16{col 41}{space 2}      0.0036
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}    354.12{col 28}{space 2}     282.39{col 41}{space 2}      0.0035
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1}   1064.01{col 28}{space 2}      93.98{col 41}{space 2}      0.0106
{txt}{space 7}shelter {c |}{col 16}{res}{space 1}   3280.85{col 28}{space 2}      30.48{col 41}{space 2}      0.0328
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1}  10220.13{col 28}{space 2}       9.78{col 41}{space 2}      0.1022
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}   1696.79{col 28}{space 2}      58.93{col 41}{space 2}      0.0170
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1}   1662.38{col 28}{space 2}      60.15{col 41}{space 2}      0.0166
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}   1541.72{col 28}{space 2}      64.86{col 41}{space 2}      0.0154
{txt}expert_frame_h {c |}{col 16}{res}{space 1}   1365.11{col 28}{space 2}      73.25{col 41}{space 2}      0.0137
{txt}{space 9}white {c |}{col 16}{res}{space 1}   3203.48{col 28}{space 2}      31.22{col 41}{space 2}      0.0320
{txt}{space 5}education {c |}{col 16}{res}{space 1}   2817.97{col 28}{space 2}      35.49{col 41}{space 2}      0.0282
{txt}{space 11}gop {c |}{col 16}{res}{space 1}  11047.11{col 28}{space 2}       9.05{col 41}{space 2}      0.1105
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1}    243.43{col 28}{space 2}     410.80{col 41}{space 2}      0.0024
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1}    423.89{col 28}{space 2}     235.91{col 41}{space 2}      0.0042
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1}    397.30{col 28}{space 2}     251.70{col 41}{space 2}      0.0040
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1}    369.51{col 28}{space 2}     270.63{col 41}{space 2}      0.0037
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}    361.73{col 28}{space 2}     276.45{col 41}{space 2}      0.0036
{txt}{space 9}_cons {c |}{col 16}{res}{space 1}    254.38{col 28}{space 2}     393.11{col 41}{space 2}      0.0025
{txt}{hline 15}{c BT}{hline 11}{hline 13}{hline 14}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99777{col 25}{space 2}   0.04717{col 37}{space 2} .0005779
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: expert_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}    .5955{col 25}{space 2}   0.49080{col 37}{space 2} .0046451
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: cdc_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96471{col 25}{space 2}   0.18451{col 37}{space 2} .0012201
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: pres_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99988{col 25}{space 2}   0.01095{col 37}{space 2} .0000424
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .94607{col 25}{space 2}   0.22588{col 37}{space 2} .0015756
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: gop{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:gop{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .84187{col 25}{space 2}   0.36486{col 37}{space 2} .0029521
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: pres_m{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96461{col 25}{space 2}   0.18476{col 37}{space 2} .0050932
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: pres_m{c )-}+{c -(}no_shop:pres_frame_h{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-}+{c -(}no_shop:pres_f rame_h{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .91581{col 25}{space 2}   0.27767{col 37}{space 2} .0073046
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. estimates drop m2
{err}estimation result m2 not found
{txt}{search r(111), local:r(111);}

{com}. ologit distance cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1295.0831}  
Iteration 2:{space 3}log likelihood = {res:-1294.8312}  
Iteration 3:{space 3}log likelihood = {res:-1294.8311}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     57.02
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1294.8311{txt}{col 49}Pseudo R2{col 67}= {res}    0.0215

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .1711818{col 28}{space 2} .5778217{col 39}{space 1}    0.30{col 48}{space 3}0.767{col 56}{space 4} -.961328{col 69}{space 3} 1.303692
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.3948252{col 28}{space 2} .5314688{col 39}{space 1}   -0.74{col 48}{space 3}0.458{col 56}{space 4}-1.436485{col 69}{space 3} .6468344
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} -.916809{col 28}{space 2}  .538472{col 39}{space 1}   -1.70{col 48}{space 3}0.089{col 56}{space 4}-1.972195{col 69}{space 3} .1385767
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .7568701{col 28}{space 2} .5558291{col 39}{space 1}    1.36{col 48}{space 3}0.173{col 56}{space 4}-.3325349{col 69}{space 3} 1.846275
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .1198902{col 28}{space 2} .2556018{col 39}{space 1}    0.47{col 48}{space 3}0.639{col 56}{space 4}  -.38108{col 69}{space 3} .6208604
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0436487{col 28}{space 2} .1378707{col 39}{space 1}    0.32{col 48}{space 3}0.752{col 56}{space 4}-.2265729{col 69}{space 3} .3138704
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1934587{col 28}{space 2} .1247799{col 39}{space 1}    1.55{col 48}{space 3}0.121{col 56}{space 4}-.0511054{col 69}{space 3} .4380227
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.0776357{col 28}{space 2} .3660585{col 39}{space 1}   -0.21{col 48}{space 3}0.832{col 56}{space 4} -.795097{col 69}{space 3} .6398257
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .3070978{col 28}{space 2} .3531741{col 39}{space 1}    0.87{col 48}{space 3}0.385{col 56}{space 4}-.3851107{col 69}{space 3} .9993063
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} .1599423{col 28}{space 2} .3646116{col 39}{space 1}    0.44{col 48}{space 3}0.661{col 56}{space 4}-.5546833{col 69}{space 3} .8745679
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.2847009{col 28}{space 2} .3554974{col 39}{space 1}   -0.80{col 48}{space 3}0.423{col 56}{space 4}-.9814629{col 69}{space 3} .4120612
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4229535{col 28}{space 2} .1381139{col 39}{space 1}    3.06{col 48}{space 3}0.002{col 56}{space 4} .1522552{col 69}{space 3} .6936518
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1000961{col 28}{space 2} .0405778{col 39}{space 1}    2.47{col 48}{space 3}0.014{col 56}{space 4}  .020565{col 69}{space 3} .1796271
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1545605{col 28}{space 2}  .123613{col 39}{space 1}   -1.25{col 48}{space 3}0.211{col 56}{space 4}-.3968375{col 69}{space 3} .0877164
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0110277{col 28}{space 2} .0048819{col 39}{space 1}    2.26{col 48}{space 3}0.024{col 56}{space 4} .0014595{col 69}{space 3}  .020596
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2}-.0020888{col 28}{space 2} .0073967{col 39}{space 1}   -0.28{col 48}{space 3}0.778{col 56}{space 4} -.016586{col 69}{space 3} .0124085
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0015158{col 28}{space 2} .0068111{col 39}{space 1}    0.22{col 48}{space 3}0.824{col 56}{space 4}-.0118337{col 69}{space 3} .0148652
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0109759{col 28}{space 2}   .00713{col 39}{space 1}    1.54{col 48}{space 3}0.124{col 56}{space 4}-.0029987{col 69}{space 3} .0249505
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2}-.0087655{col 28}{space 2} .0070771{col 39}{space 1}   -1.24{col 48}{space 3}0.216{col 56}{space 4}-.0226363{col 69}{space 3} .0051054
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-2.621328{col 28}{space 2} .4481032{col 56}{space 4}-3.499594{col 69}{space 3}-1.743062
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.589506{col 28}{space 2} .4180069{col 56}{space 4}-2.408784{col 69}{space 3}-.7702271
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.6829284{col 28}{space 2} .4090389{col 56}{space 4} -1.48463{col 69}{space 3} .1187732
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} .8906096{col 28}{space 2} .4079655{col 56}{space 4} .0910119{col 69}{space 3} 1.690207
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum gop dem

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}gop {c |}{res}      1,348    .3227003    .4676827          0          1
{txt}{space 9}dem {c |}{res}      1,348     .398368    .4897437          0          1

{com}. tab income

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        309       22.92       22.92
{txt}          2 {c |}{res}        212       15.73       38.65
{txt}          3 {c |}{res}        187       13.87       52.52
{txt}          4 {c |}{res}        156       11.57       64.09
{txt}          5 {c |}{res}        121        8.98       73.07
{txt}          6 {c |}{res}        100        7.42       80.49
{txt}          7 {c |}{res}        263       19.51      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. tab education

  {txt}education {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        250       18.55       18.55
{txt}          2 {c |}{res}        340       25.22       43.77
{txt}          3 {c |}{res}        153       11.35       55.12
{txt}          4 {c |}{res}        371       27.52       82.64
{txt}          5 {c |}{res}        234       17.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. tab age

        {txt}age {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}        176       13.06       13.06
{txt}          3 {c |}{res}        306       22.70       35.76
{txt}          4 {c |}{res}        323       23.96       59.72
{txt}          5 {c |}{res}        196       14.54       74.26
{txt}          6 {c |}{res}        188       13.95       88.20
{txt}          7 {c |}{res}        143       10.61       98.81
{txt}          8 {c |}{res}         16        1.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. sub jobloss
{err}command {bf}sub{sf} is unrecognized
{txt}{search r(199), local:r(199);}

{com}. sum jobloss

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}jobloss {c |}{res}      1,348    .3048961    .4605343          0          1

{com}. sum shelter

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}shelter {c |}{res}      1,348    .7908012    .4068876          0          1

{com}. regress treatment gender education income age white gop

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,346
{txt}{hline 13}{c +}{hline 34}   F(6, 1339)      = {res}     0.58
{txt}       Model {c |} {res} 28.3811385         6  4.73018976   {txt}Prob > F        ={res}    0.7472
{txt}    Residual {c |} {res} 10935.8781     1,339  8.16719802   {txt}R-squared       ={res}    0.0026
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0019
{txt}       Total {c |} {res} 10964.2593     1,345  8.15186564   {txt}Root MSE        =   {res} 2.8578

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   treatment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2} .1152624{col 26}{space 2} .1591155{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.1968803{col 67}{space 3} .4274052
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0478141{col 26}{space 2} .0630241{col 37}{space 1}   -0.76{col 46}{space 3}0.448{col 54}{space 4}-.1714508{col 67}{space 3} .0758226
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0446468{col 26}{space 2} .0399027{col 37}{space 1}    1.12{col 46}{space 3}0.263{col 54}{space 4}-.0336317{col 67}{space 3} .1229254
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0496777{col 26}{space 2} .0506445{col 37}{space 1}    0.98{col 46}{space 3}0.327{col 54}{space 4}-.0496736{col 67}{space 3} .1490289
{txt}{space 7}white {c |}{col 14}{res}{space 2}-.1775669{col 26}{space 2} .1989541{col 37}{space 1}   -0.89{col 46}{space 3}0.372{col 54}{space 4}-.5678625{col 67}{space 3} .2127287
{txt}{space 9}gop {c |}{col 14}{res}{space 2} .0580759{col 26}{space 2}  .172491{col 37}{space 1}    0.34{col 46}{space 3}0.736{col 54}{space 4}-.2803061{col 67}{space 3} .3964579
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.345832{col 26}{space 2} .2946136{col 37}{space 1}   18.15{col 46}{space 3}0.000{col 54}{space 4} 4.767878{col 67}{space 3} 5.923787
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. ologit treatment gender education income age white gop

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3094.9011}  
Iteration 1:{space 3}log likelihood = {res:-3093.1719}  
Iteration 2:{space 3}log likelihood = {res:-3093.1718}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}6{txt}){col 67}= {res}      3.46
{txt}{col 49}Prob > chi2{col 67}= {res}    0.7495
{txt}Log likelihood = {res}-3093.1718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0006

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   treatment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}gender {c |}{col 14}{res}{space 2} .0702245{col 26}{space 2} .0972818{col 37}{space 1}    0.72{col 46}{space 3}0.470{col 54}{space 4}-.1204444{col 67}{space 3} .2608934
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0280717{col 26}{space 2} .0387181{col 37}{space 1}   -0.73{col 46}{space 3}0.468{col 54}{space 4}-.1039578{col 67}{space 3} .0478144
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0260182{col 26}{space 2} .0242225{col 37}{space 1}    1.07{col 46}{space 3}0.283{col 54}{space 4} -.021457{col 67}{space 3} .0734935
{txt}{space 9}age {c |}{col 14}{res}{space 2}  .030642{col 26}{space 2} .0307669{col 37}{space 1}    1.00{col 46}{space 3}0.319{col 54}{space 4}-.0296601{col 67}{space 3} .0909441
{txt}{space 7}white {c |}{col 14}{res}{space 2}-.1082337{col 26}{space 2} .1220756{col 37}{space 1}   -0.89{col 46}{space 3}0.375{col 54}{space 4}-.3474974{col 67}{space 3}   .13103
{txt}{space 9}gop {c |}{col 14}{res}{space 2} .0334286{col 26}{space 2} .1053194{col 37}{space 1}    0.32{col 46}{space 3}0.751{col 54}{space 4}-.1729937{col 67}{space 3}  .239851
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.104102{col 26}{space 2} .1958492{col 54}{space 4}-2.487959{col 67}{space 3}-1.720244
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} -1.34062{col 26}{space 2} .1868424{col 54}{space 4}-1.706824{col 67}{space 3}-.9744152
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.7901697{col 26}{space 2} .1836423{col 54}{space 4}-1.150102{col 67}{space 3}-.4302373
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}-.2909443{col 26}{space 2} .1824718{col 54}{space 4}-.6485825{col 67}{space 3} .0666938
{txt}{space 7}/cut5 {c |}{col 14}{res}{space 2} .1277533{col 26}{space 2} .1822982{col 54}{space 4}-.2295446{col 67}{space 3} .4850511
{txt}{space 7}/cut6 {c |}{col 14}{res}{space 2} .4865231{col 26}{space 2} .1826002{col 54}{space 4} .1286333{col 67}{space 3} .8444128
{txt}{space 7}/cut7 {c |}{col 14}{res}{space 2} .9035277{col 26}{space 2} .1838558{col 54}{space 4} .5431768{col 67}{space 3} 1.263879
{txt}{space 7}/cut8 {c |}{col 14}{res}{space 2} 1.486517{col 26}{space 2} .1871215{col 54}{space 4} 1.119766{col 67}{space 3} 1.853268
{txt}{space 7}/cut9 {c |}{col 14}{res}{space 2} 2.410873{col 26}{space 2} .1986138{col 54}{space 4} 2.021597{col 67}{space 3} 2.800149
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum distance trust knowledge

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}distance {c |}{res}      1,346    4.462853    .8770696          1          5
{txt}{space 7}trust {c |}{res}      1,346    4.131501     .869014          1          5
{txt}{space 3}knowledge {c |}{res}      1,346     4.17162    .8240601          1          5

{com}. ologit trust cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1579.9071}  
Iteration 1:{space 3}log likelihood = {res:-1530.3912}  
Iteration 2:{space 3}log likelihood = {res: -1530.126}  
Iteration 3:{space 3}log likelihood = {res:-1530.1259}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     99.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1530.1259{txt}{col 49}Pseudo R2{col 67}= {res}    0.0315

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         trust{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-.9772216{col 28}{space 2} .5362084{col 39}{space 1}   -1.82{col 48}{space 3}0.068{col 56}{space 4}-2.028171{col 69}{space 3} .0737275
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.7457742{col 28}{space 2} .5016413{col 39}{space 1}   -1.49{col 48}{space 3}0.137{col 56}{space 4}-1.728973{col 69}{space 3} .2374246
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.6258461{col 28}{space 2} .5051038{col 39}{space 1}   -1.24{col 48}{space 3}0.215{col 56}{space 4}-1.615831{col 69}{space 3} .3641391
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.1280588{col 28}{space 2}  .510196{col 39}{space 1}   -0.25{col 48}{space 3}0.802{col 56}{space 4}-1.128025{col 69}{space 3}  .871907
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}-.6629128{col 28}{space 2}  .227858{col 39}{space 1}   -2.91{col 48}{space 3}0.004{col 56}{space 4}-1.109506{col 69}{space 3}-.2163194
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .3262723{col 28}{space 2} .1278348{col 39}{space 1}    2.55{col 48}{space 3}0.011{col 56}{space 4} .0757207{col 69}{space 3} .5768239
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1254684{col 28}{space 2} .1134057{col 39}{space 1}    1.11{col 48}{space 3}0.269{col 56}{space 4}-.0968028{col 69}{space 3} .3477395
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} 1.268964{col 28}{space 2} .3330096{col 39}{space 1}    3.81{col 48}{space 3}0.000{col 56}{space 4} .6162774{col 69}{space 3} 1.921651
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2}  1.05733{col 28}{space 2} .3219265{col 39}{space 1}    3.28{col 48}{space 3}0.001{col 56}{space 4} .4263653{col 69}{space 3} 1.688294
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} .7257551{col 28}{space 2} .3252913{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .0881958{col 69}{space 3} 1.363314
{txt}expert_frame_h {c |}{col 16}{res}{space 2} .6774604{col 28}{space 2} .3237044{col 39}{space 1}    2.09{col 48}{space 3}0.036{col 56}{space 4} .0430115{col 69}{space 3} 1.311909
{txt}{space 9}white {c |}{col 16}{res}{space 2}-.0289077{col 28}{space 2} .1315729{col 39}{space 1}   -0.22{col 48}{space 3}0.826{col 56}{space 4}-.2867858{col 69}{space 3} .2289704
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1288548{col 28}{space 2} .0371196{col 39}{space 1}    3.47{col 48}{space 3}0.001{col 56}{space 4} .0561017{col 69}{space 3} .2016079
{txt}{space 11}gop {c |}{col 16}{res}{space 2} .3026984{col 28}{space 2} .1139832{col 39}{space 1}    2.66{col 48}{space 3}0.008{col 56}{space 4} .0792954{col 69}{space 3} .5261013
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0106866{col 28}{space 2} .0044659{col 39}{space 1}    2.39{col 48}{space 3}0.017{col 56}{space 4} .0019336{col 69}{space 3} .0194396
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2} .0095923{col 28}{space 2} .0068039{col 39}{space 1}    1.41{col 48}{space 3}0.159{col 56}{space 4}-.0037431{col 69}{space 3} .0229277
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0025956{col 28}{space 2} .0063319{col 39}{space 1}    0.41{col 48}{space 3}0.682{col 56}{space 4}-.0098148{col 69}{space 3} .0150059
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0037184{col 28}{space 2} .0065235{col 39}{space 1}    0.57{col 48}{space 3}0.569{col 56}{space 4}-.0090674{col 69}{space 3} .0165041
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0001861{col 28}{space 2} .0064823{col 39}{space 1}    0.03{col 48}{space 3}0.977{col 56}{space 4}-.0125189{col 69}{space 3} .0128912
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-3.016345{col 28}{space 2} .4350402{col 56}{space 4}-3.869009{col 69}{space 3}-2.163682
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.882799{col 28}{space 2} .3957462{col 56}{space 4}-2.658448{col 69}{space 3}-1.107151
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.2608863{col 28}{space 2}  .381798{col 56}{space 4}-1.009197{col 69}{space 3}  .487424
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 1.829613{col 28}{space 2} .3849996{col 56}{space 4} 1.075028{col 69}{space 3} 2.584199
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -1521.695}  
Iteration 1:{space 3}log likelihood = {res:-1463.1546}  
Iteration 2:{space 3}log likelihood = {res:-1462.7483}  
Iteration 3:{space 3}log likelihood = {res: -1462.748}  
Iteration 4:{space 3}log likelihood = {res: -1462.748}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}    117.89
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -1462.748{txt}{col 49}Pseudo R2{col 67}= {res}    0.0387

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-1.103729{col 28}{space 2}  .542844{col 39}{space 1}   -2.03{col 48}{space 3}0.042{col 56}{space 4}-2.167684{col 69}{space 3}-.0397745
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-1.077542{col 28}{space 2} .5024848{col 39}{space 1}   -2.14{col 48}{space 3}0.032{col 56}{space 4}-2.062394{col 69}{space 3}-.0926898
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.7885891{col 28}{space 2} .5052501{col 39}{space 1}   -1.56{col 48}{space 3}0.119{col 56}{space 4}-1.778861{col 69}{space 3}  .201683
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.1323093{col 28}{space 2} .5175412{col 39}{space 1}   -0.26{col 48}{space 3}0.798{col 56}{space 4}-1.146671{col 69}{space 3} .8820528
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}-.6074254{col 28}{space 2} .2311628{col 39}{space 1}   -2.63{col 48}{space 3}0.009{col 56}{space 4}-1.060496{col 69}{space 3}-.1543547
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}  .270719{col 28}{space 2} .1289744{col 39}{space 1}    2.10{col 48}{space 3}0.036{col 56}{space 4} .0179339{col 69}{space 3} .5235041
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1345121{col 28}{space 2} .1145843{col 39}{space 1}    1.17{col 48}{space 3}0.240{col 56}{space 4}-.0900689{col 69}{space 3} .3590932
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} 1.379587{col 28}{space 2} .3372012{col 39}{space 1}    4.09{col 48}{space 3}0.000{col 56}{space 4} .7186843{col 69}{space 3} 2.040489
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} 1.142525{col 28}{space 2} .3254717{col 39}{space 1}    3.51{col 48}{space 3}0.000{col 56}{space 4} .5046123{col 69}{space 3} 1.780438
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} .5757273{col 28}{space 2} .3287747{col 39}{space 1}    1.75{col 48}{space 3}0.080{col 56}{space 4}-.0686592{col 69}{space 3} 1.220114
{txt}expert_frame_h {c |}{col 16}{res}{space 2} .6427874{col 28}{space 2}  .327934{col 39}{space 1}    1.96{col 48}{space 3}0.050{col 56}{space 4} .0000486{col 69}{space 3} 1.285526
{txt}{space 9}white {c |}{col 16}{res}{space 2}-.1318647{col 28}{space 2} .1325022{col 39}{space 1}   -1.00{col 48}{space 3}0.320{col 56}{space 4}-.3915642{col 69}{space 3} .1278348
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1325497{col 28}{space 2} .0376688{col 39}{space 1}    3.52{col 48}{space 3}0.000{col 56}{space 4} .0587201{col 69}{space 3} .2063792
{txt}{space 11}gop {c |}{col 16}{res}{space 2} .3340158{col 28}{space 2} .1149788{col 39}{space 1}    2.91{col 48}{space 3}0.004{col 56}{space 4} .1086615{col 69}{space 3} .5593702
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0096656{col 28}{space 2} .0045221{col 39}{space 1}    2.14{col 48}{space 3}0.033{col 56}{space 4} .0008025{col 69}{space 3} .0185287
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2} .0115325{col 28}{space 2} .0068851{col 39}{space 1}    1.67{col 48}{space 3}0.094{col 56}{space 4}-.0019621{col 69}{space 3} .0250271
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0078505{col 28}{space 2} .0063836{col 39}{space 1}    1.23{col 48}{space 3}0.219{col 56}{space 4}-.0046612{col 69}{space 3} .0203621
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0054515{col 28}{space 2} .0065313{col 39}{space 1}    0.83{col 48}{space 3}0.404{col 56}{space 4}-.0073497{col 69}{space 3} .0182527
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0026734{col 28}{space 2} .0065824{col 39}{space 1}    0.41{col 48}{space 3}0.685{col 56}{space 4}-.0102279{col 69}{space 3} .0155747
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-3.377743{col 28}{space 2} .4492205{col 56}{space 4}-4.258199{col 69}{space 3}-2.497287
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-2.363128{col 28}{space 2} .4033323{col 56}{space 4}-3.153645{col 69}{space 3}-1.572611
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.5085117{col 28}{space 2} .3807251{col 56}{space 4}-1.254719{col 69}{space 3} .2376958
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 1.701109{col 28}{space 2} .3833467{col 56}{space 4} .9497635{col 69}{space 3} 2.452455
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4474
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .002434
{col 65}{txt}avg ={col 71}{res}    .02074
{txt}Log marginal-likelihood = {res}-841.05008{col 65}{txt}max ={col 71}{res}     .1105
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .2831857{col 27}{space 2} .6430288{col 38}{space 2} .031656{col 48}{space 2} .2779978{col 59}{space 2}-.9659911{col 70}{space 2} 1.546595
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.043622{col 27}{space 2} .5722402{col 38}{space 2} .028667{col 48}{space 2}-1.046234{col 59}{space 2}  -2.1435{col 70}{space 2} .0947145
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.7537072{col 27}{space 2} .5939124{col 38}{space 2} .031492{col 48}{space 2}-.7551286{col 59}{space 2}-1.906793{col 70}{space 2} .4354907
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.3953186{col 27}{space 2} .5990663{col 38}{space 2} .031835{col 48}{space 2}-.4040052{col 59}{space 2}-1.569546{col 70}{space 2} .7992103
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8199422{col 27}{space 2} .2955073{col 38}{space 2} .009059{col 48}{space 2} .8205362{col 59}{space 2} .2452172{col 70}{space 2} 1.398247
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0404143{col 27}{space 2} .1602751{col 38}{space 2} .002798{col 48}{space 2} .0401811{col 59}{space 2}-.2751674{col 70}{space 2} .3505788
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0060619{col 27}{space 2}  .139951{col 38}{space 2} .001384{col 48}{space 2}  .005344{col 59}{space 2} -.267529{col 70}{space 2} .2813095
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2619147{col 27}{space 2}  .427937{col 38}{space 2} .010389{col 48}{space 2}-.2686857{col 59}{space 2}-1.083762{col 70}{space 2} .5898811
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .2146161{col 27}{space 2} .4169351{col 38}{space 2} .010226{col 48}{space 2} .2135961{col 59}{space 2}-.5966479{col 70}{space 2}  1.03871
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3428776{col 27}{space 2}  .419886{col 38}{space 2} .010694{col 48}{space 2}-.3408507{col 59}{space 2}-1.165187{col 70}{space 2} .4642733
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7511599{col 27}{space 2} .4066276{col 38}{space 2} .011006{col 48}{space 2}-.7540468{col 59}{space 2}-1.540584{col 70}{space 2} .0572072
{txt}{space 9}white {c |}{col 16}{res}{space 1}  .458307{col 27}{space 2} .1574497{col 38}{space 2} .002782{col 48}{space 2} .4576138{col 59}{space 2} .1483474{col 70}{space 2}  .765743
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0256864{col 27}{space 2} .0457545{col 38}{space 2} .000862{col 48}{space 2} .0258528{col 59}{space 2} -.064209{col 70}{space 2}  .115115
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1414013{col 27}{space 2} .1411476{col 38}{space 2} .001343{col 48}{space 2} -.141107{col 59}{space 2}-.4178767{col 70}{space 2} .1348183
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0007763{col 27}{space 2} .0051757{col 38}{space 2} .000332{col 48}{space 2} .0008039{col 59}{space 2}-.0092649{col 70}{space 2} .0109156
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0003336{col 27}{space 2} .0081698{col 38}{space 2} .000397{col 48}{space 2} .0003293{col 59}{space 2}-.0155378{col 70}{space 2} .0164999
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0125765{col 27}{space 2} .0073023{col 38}{space 2} .000366{col 48}{space 2} .0125588{col 59}{space 2}-.0019417{col 70}{space 2} .0266675
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0126454{col 27}{space 2} .0077527{col 38}{space 2} .000403{col 48}{space 2} .0126323{col 59}{space 2}-.0024616{col 70}{space 2} .0277134
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1}  .008659{col 27}{space 2} .0077178{col 38}{space 2} .000406{col 48}{space 2}  .008736{col 59}{space 2}-.0066271{col 70}{space 2} .0237316
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .2531256{col 27}{space 2} .4325913{col 38}{space 2} .027123{col 48}{space 2} .2512392{col 59}{space 2}-.5903438{col 70}{space 2} 1.114707
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayestest interval {c -(}no_shop: pres_m{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96461{col 25}{space 2}   0.18476{col 37}{space 2} .0050932
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: pres_m{c )-}+{c -(}no_shop:pres_frame_h{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-}+{c -(}no_shop:pres_f rame_h{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .91581{col 25}{space 2}   0.27767{col 37}{space 2} .0073046
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: pres_m{c )-}+{c -(}no_shop: pres_ideol{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-}+{c -(}no_shop:pres_i deol{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}    .9645{col 25}{space 2}   0.18504{col 37}{space 2} .0050992
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99777{col 25}{space 2}   0.04717{col 37}{space 2} .0005779
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: expert_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}    .5955{col 25}{space 2}   0.49080{col 37}{space 2} .0046451
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: cdc_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96471{col 25}{space 2}   0.18451{col 37}{space 2} .0012201
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: pres_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99988{col 25}{space 2}   0.01095{col 37}{space 2} .0000424
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .94607{col 25}{space 2}   0.22588{col 37}{space 2} .0015756
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: gop{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:gop{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .84187{col 25}{space 2}   0.36486{col 37}{space 2} .0029521
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: cdc_m _m{c )-}+{c -(}no_shop: cdc_frame_h {c )-}, upper(0)
{res}{err}invalid expression {bf:{c -(}no_shop:cdc_m{c )-} {c -(}no_shop:_m{c )-}+{c -(}no_shop:cdc_frame_h{c )-} < 0}
{txt}{search r(111), local:r(111);}

{com}. bayestest interval {c -(}no_shop:cdc_m _m{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}{err}invalid expression {bf:{c -(}no_shop:cdc_m{c )-} {c -(}no_shop:_m{c )-}+{c -(}no_shop:cdc_frame_h{c )-} > 0}
{txt}{search r(111), local:r(111);}

{com}. bayestest interval {c -(}no_shop:cdc_m{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:cdc_m{c )-}+{c -(}no_shop:cdc_fra me_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .51278{col 25}{space 2}   0.49984{col 37}{space 2} .0162783
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: state_m {c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:state_m{c )-}+{c -(}no_shop:state _frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .03986{col 25}{space 2}   0.19563{col 37}{space 2} .0043919
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: state_m {c )-}+{c -(}no_shop:state_frame_h{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:state_m{c )-}+{c -(}no_shop:state _frame_h{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96014{col 25}{space 2}   0.19563{col 37}{space 2} .0043919
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .94607{col 25}{space 2}   0.22588{col 37}{space 2} .0015756
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99777{col 25}{space 2}   0.04717{col 37}{space 2} .0005779
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: expert_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}    .5955{col 25}{space 2}   0.49080{col 37}{space 2} .0046451
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: cdc_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96471{col 25}{space 2}   0.18451{col 37}{space 2} .0012201
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: pres_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99988{col 25}{space 2}   0.01095{col 37}{space 2} .0000424
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .94607{col 25}{space 2}   0.22588{col 37}{space 2} .0015756
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. ologit trust cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1579.9071}  
Iteration 1:{space 3}log likelihood = {res:-1530.3912}  
Iteration 2:{space 3}log likelihood = {res: -1530.126}  
Iteration 3:{space 3}log likelihood = {res:-1530.1259}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     99.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1530.1259{txt}{col 49}Pseudo R2{col 67}= {res}    0.0315

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         trust{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-.9772216{col 28}{space 2} .5362084{col 39}{space 1}   -1.82{col 48}{space 3}0.068{col 56}{space 4}-2.028171{col 69}{space 3} .0737275
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.7457742{col 28}{space 2} .5016413{col 39}{space 1}   -1.49{col 48}{space 3}0.137{col 56}{space 4}-1.728973{col 69}{space 3} .2374246
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.6258461{col 28}{space 2} .5051038{col 39}{space 1}   -1.24{col 48}{space 3}0.215{col 56}{space 4}-1.615831{col 69}{space 3} .3641391
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.1280588{col 28}{space 2}  .510196{col 39}{space 1}   -0.25{col 48}{space 3}0.802{col 56}{space 4}-1.128025{col 69}{space 3}  .871907
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}-.6629128{col 28}{space 2}  .227858{col 39}{space 1}   -2.91{col 48}{space 3}0.004{col 56}{space 4}-1.109506{col 69}{space 3}-.2163194
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .3262723{col 28}{space 2} .1278348{col 39}{space 1}    2.55{col 48}{space 3}0.011{col 56}{space 4} .0757207{col 69}{space 3} .5768239
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1254684{col 28}{space 2} .1134057{col 39}{space 1}    1.11{col 48}{space 3}0.269{col 56}{space 4}-.0968028{col 69}{space 3} .3477395
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} 1.268964{col 28}{space 2} .3330096{col 39}{space 1}    3.81{col 48}{space 3}0.000{col 56}{space 4} .6162774{col 69}{space 3} 1.921651
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2}  1.05733{col 28}{space 2} .3219265{col 39}{space 1}    3.28{col 48}{space 3}0.001{col 56}{space 4} .4263653{col 69}{space 3} 1.688294
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} .7257551{col 28}{space 2} .3252913{col 39}{space 1}    2.23{col 48}{space 3}0.026{col 56}{space 4} .0881958{col 69}{space 3} 1.363314
{txt}expert_frame_h {c |}{col 16}{res}{space 2} .6774604{col 28}{space 2} .3237044{col 39}{space 1}    2.09{col 48}{space 3}0.036{col 56}{space 4} .0430115{col 69}{space 3} 1.311909
{txt}{space 9}white {c |}{col 16}{res}{space 2}-.0289077{col 28}{space 2} .1315729{col 39}{space 1}   -0.22{col 48}{space 3}0.826{col 56}{space 4}-.2867858{col 69}{space 3} .2289704
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1288548{col 28}{space 2} .0371196{col 39}{space 1}    3.47{col 48}{space 3}0.001{col 56}{space 4} .0561017{col 69}{space 3} .2016079
{txt}{space 11}gop {c |}{col 16}{res}{space 2} .3026984{col 28}{space 2} .1139832{col 39}{space 1}    2.66{col 48}{space 3}0.008{col 56}{space 4} .0792954{col 69}{space 3} .5261013
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0106866{col 28}{space 2} .0044659{col 39}{space 1}    2.39{col 48}{space 3}0.017{col 56}{space 4} .0019336{col 69}{space 3} .0194396
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2} .0095923{col 28}{space 2} .0068039{col 39}{space 1}    1.41{col 48}{space 3}0.159{col 56}{space 4}-.0037431{col 69}{space 3} .0229277
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0025956{col 28}{space 2} .0063319{col 39}{space 1}    0.41{col 48}{space 3}0.682{col 56}{space 4}-.0098148{col 69}{space 3} .0150059
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0037184{col 28}{space 2} .0065235{col 39}{space 1}    0.57{col 48}{space 3}0.569{col 56}{space 4}-.0090674{col 69}{space 3} .0165041
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0001861{col 28}{space 2} .0064823{col 39}{space 1}    0.03{col 48}{space 3}0.977{col 56}{space 4}-.0125189{col 69}{space 3} .0128912
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-3.016345{col 28}{space 2} .4350402{col 56}{space 4}-3.869009{col 69}{space 3}-2.163682
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.882799{col 28}{space 2} .3957462{col 56}{space 4}-2.658448{col 69}{space 3}-1.107151
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.2608863{col 28}{space 2}  .381798{col 56}{space 4}-1.009197{col 69}{space 3}  .487424
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 1.829613{col 28}{space 2} .3849996{col 56}{space 4} 1.075028{col 69}{space 3} 2.584199
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -1521.695}  
Iteration 1:{space 3}log likelihood = {res:-1463.1546}  
Iteration 2:{space 3}log likelihood = {res:-1462.7483}  
Iteration 3:{space 3}log likelihood = {res: -1462.748}  
Iteration 4:{space 3}log likelihood = {res: -1462.748}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}    117.89
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -1462.748{txt}{col 49}Pseudo R2{col 67}= {res}    0.0387

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     knowledge{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-1.103729{col 28}{space 2}  .542844{col 39}{space 1}   -2.03{col 48}{space 3}0.042{col 56}{space 4}-2.167684{col 69}{space 3}-.0397745
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-1.077542{col 28}{space 2} .5024848{col 39}{space 1}   -2.14{col 48}{space 3}0.032{col 56}{space 4}-2.062394{col 69}{space 3}-.0926898
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.7885891{col 28}{space 2} .5052501{col 39}{space 1}   -1.56{col 48}{space 3}0.119{col 56}{space 4}-1.778861{col 69}{space 3}  .201683
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.1323093{col 28}{space 2} .5175412{col 39}{space 1}   -0.26{col 48}{space 3}0.798{col 56}{space 4}-1.146671{col 69}{space 3} .8820528
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}-.6074254{col 28}{space 2} .2311628{col 39}{space 1}   -2.63{col 48}{space 3}0.009{col 56}{space 4}-1.060496{col 69}{space 3}-.1543547
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}  .270719{col 28}{space 2} .1289744{col 39}{space 1}    2.10{col 48}{space 3}0.036{col 56}{space 4} .0179339{col 69}{space 3} .5235041
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1345121{col 28}{space 2} .1145843{col 39}{space 1}    1.17{col 48}{space 3}0.240{col 56}{space 4}-.0900689{col 69}{space 3} .3590932
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} 1.379587{col 28}{space 2} .3372012{col 39}{space 1}    4.09{col 48}{space 3}0.000{col 56}{space 4} .7186843{col 69}{space 3} 2.040489
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} 1.142525{col 28}{space 2} .3254717{col 39}{space 1}    3.51{col 48}{space 3}0.000{col 56}{space 4} .5046123{col 69}{space 3} 1.780438
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2} .5757273{col 28}{space 2} .3287747{col 39}{space 1}    1.75{col 48}{space 3}0.080{col 56}{space 4}-.0686592{col 69}{space 3} 1.220114
{txt}expert_frame_h {c |}{col 16}{res}{space 2} .6427874{col 28}{space 2}  .327934{col 39}{space 1}    1.96{col 48}{space 3}0.050{col 56}{space 4} .0000486{col 69}{space 3} 1.285526
{txt}{space 9}white {c |}{col 16}{res}{space 2}-.1318647{col 28}{space 2} .1325022{col 39}{space 1}   -1.00{col 48}{space 3}0.320{col 56}{space 4}-.3915642{col 69}{space 3} .1278348
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1325497{col 28}{space 2} .0376688{col 39}{space 1}    3.52{col 48}{space 3}0.000{col 56}{space 4} .0587201{col 69}{space 3} .2063792
{txt}{space 11}gop {c |}{col 16}{res}{space 2} .3340158{col 28}{space 2} .1149788{col 39}{space 1}    2.91{col 48}{space 3}0.004{col 56}{space 4} .1086615{col 69}{space 3} .5593702
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0096656{col 28}{space 2} .0045221{col 39}{space 1}    2.14{col 48}{space 3}0.033{col 56}{space 4} .0008025{col 69}{space 3} .0185287
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2} .0115325{col 28}{space 2} .0068851{col 39}{space 1}    1.67{col 48}{space 3}0.094{col 56}{space 4}-.0019621{col 69}{space 3} .0250271
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0078505{col 28}{space 2} .0063836{col 39}{space 1}    1.23{col 48}{space 3}0.219{col 56}{space 4}-.0046612{col 69}{space 3} .0203621
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0054515{col 28}{space 2} .0065313{col 39}{space 1}    0.83{col 48}{space 3}0.404{col 56}{space 4}-.0073497{col 69}{space 3} .0182527
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2} .0026734{col 28}{space 2} .0065824{col 39}{space 1}    0.41{col 48}{space 3}0.685{col 56}{space 4}-.0102279{col 69}{space 3} .0155747
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-3.377743{col 28}{space 2} .4492205{col 56}{space 4}-4.258199{col 69}{space 3}-2.497287
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-2.363128{col 28}{space 2} .4033323{col 56}{space 4}-3.153645{col 69}{space 3}-1.572611
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.5085117{col 28}{space 2} .3807251{col 56}{space 4}-1.254719{col 69}{space 3} .2376958
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 1.701109{col 28}{space 2} .3833467{col 56}{space 4} .9497635{col 69}{space 3} 2.452455
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: econ_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_e{c )-}) block({c -(}no_shop: pres_frame_e{c )-}) block({c -(}no_shop: state_frame_e{c )-}) block({c -(}no_shop: expert_frame_e{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: cdc_ideol{c )-}) block({c -(}no_shop: pres_ideol{c )-}) block({c -(}no_shop: state_ideol{c )-}) block({c -(}no_shop: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m econ_frame shelter jobloss cdc_frame_e pres_frame_e state_frame_e expert_frame_e white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:econ_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:state_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,348
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4515
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .002355
{col 65}{txt}avg ={col 71}{res}    .01966
{txt}Log marginal-likelihood = {res}-842.25912{col 65}{txt}max ={col 71}{res}     .1119
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .1899061{col 27}{space 2} .6989315{col 38}{space 2} .036858{col 48}{space 2} .1932258{col 59}{space 2}-1.182211{col 70}{space 2} 1.559848
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1} -.673442{col 27}{space 2} .5868602{col 38}{space 2} .030483{col 48}{space 2}-.6676117{col 59}{space 2}-1.829285{col 70}{space 2} .4660142
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.9413768{col 27}{space 2} .6244372{col 38}{space 2} .033729{col 48}{space 2}-.9320491{col 59}{space 2}-2.197695{col 70}{space 2} .2697301
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.9959888{col 27}{space 2} .5938256{col 38}{space 2} .031983{col 48}{space 2}-.9808151{col 59}{space 2}-2.220409{col 70}{space 2}  .125214
{txt}{space 4}econ_frame {c |}{col 16}{res}{space 1}-.6865244{col 27}{space 2} .2839124{col 38}{space 2} .011324{col 48}{space 2}-.6862663{col 59}{space 2}-1.239239{col 70}{space 2}-.1357495
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0424287{col 27}{space 2} .1600505{col 38}{space 2} .002883{col 48}{space 2}  .042886{col 59}{space 2}-.2751408{col 70}{space 2}  .354767
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0028631{col 27}{space 2} .1390017{col 38}{space 2} .001314{col 48}{space 2} .0022802{col 59}{space 2}-.2688575{col 70}{space 2} .2783384
{txt}{space 3}cdc_frame_e {c |}{col 16}{res}{space 1} .1285447{col 27}{space 2} .4247987{col 38}{space 2} .013659{col 48}{space 2} .1286332{col 59}{space 2}-.7105787{col 70}{space 2} .9554609
{txt}{space 2}pres_frame_e {c |}{col 16}{res}{space 1}-.3474308{col 27}{space 2} .4049119{col 38}{space 2} .012815{col 48}{space 2}-.3423876{col 59}{space 2}-1.153149{col 70}{space 2} .4418625
{txt}{space 1}state_frame_e {c |}{col 16}{res}{space 1} .2063174{col 27}{space 2} .4106111{col 38}{space 2} .013432{col 48}{space 2} .2056503{col 59}{space 2}-.6052951{col 70}{space 2} 1.013199
{txt}expert_frame_e {c |}{col 16}{res}{space 1} .6146968{col 27}{space 2} .3950226{col 38}{space 2} .012587{col 48}{space 2} .6159018{col 59}{space 2}-.1561092{col 70}{space 2} 1.385133
{txt}{space 9}white {c |}{col 16}{res}{space 1}  .466156{col 27}{space 2} .1559844{col 38}{space 2} .002723{col 48}{space 2} .4653692{col 59}{space 2} .1653249{col 70}{space 2} .7743838
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0264509{col 27}{space 2} .0455396{col 38}{space 2} .000826{col 48}{space 2}  .026546{col 59}{space 2}-.0626418{col 70}{space 2} .1150795
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.1472171{col 27}{space 2} .1420733{col 38}{space 2} .001369{col 48}{space 2}-.1470938{col 59}{space 2}-.4256805{col 70}{space 2} .1320002
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0014632{col 27}{space 2}  .005104{col 38}{space 2} .000326{col 48}{space 2} .0016461{col 59}{space 2}-.0089481{col 70}{space 2} .0112231
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1}-.0006306{col 27}{space 2} .0083933{col 38}{space 2} .000422{col 48}{space 2}-.0006801{col 59}{space 2}-.0170461{col 70}{space 2} .0160055
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0117437{col 27}{space 2} .0072787{col 38}{space 2} .000369{col 48}{space 2} .0116508{col 59}{space 2}-.0025225{col 70}{space 2} .0263575
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0118588{col 27}{space 2} .0077958{col 38}{space 2} .000396{col 48}{space 2} .0117436{col 59}{space 2} -.003292{col 70}{space 2} .0275963
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0079285{col 27}{space 2} .0076121{col 38}{space 2} .000389{col 48}{space 2} .0078372{col 59}{space 2}-.0066361{col 70}{space 2} .0232838
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .9203411{col 27}{space 2} .4530098{col 38}{space 2}  .02952{col 48}{space 2} .9146934{col 59}{space 2} .0591548{col 70}{space 2} 1.866324
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayestest interval {c -(}no_shop: pres_m{c )-}+{c -(}no_shop:pres_frame_e{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:pres_m{c )-}+{c -(}no_shop:pres_f rame_e{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96479{col 25}{space 2}   0.18431{col 37}{space 2} .0042917
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. bayes, block({c -(}trust: cdc_m{c )-}) block({c -(}trust: pres_m{c )-}) block({c -(}trust: state_m{c )-}) block({c -(}trust: expert_m{c )-}) block({c -(}trust: health_frame{c )-}) block({c -(}trust: shelter{c )-}) block({c -(}trust: jobloss{c )-}) block({c -(}trust: cdc_frame_h{c )-}) block({c -(}trust: pres_frame_h{c )-}) block({c -(}trust: state_frame_h{c )-}) block({c -(}trust: expert_frame_h{c )-}) block({c -(}trust: white{c )-}) block({c -(}trust: education{c )-}) block({c -(}trust: gop{c )-}) block({c -(}trust: ideology_rs{c )-}) block({c -(}trust: cdc_ideol{c )-}) block({c -(}trust: pres_ideol{c )-}) block({c -(}trust: state_ideol{c )-}) block({c -(}trust: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}trust:{c )-}, uniform(-10,10)): ologit trust cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 10}{space 2}{res:trust} ~ ologit(xb_trust,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 27}{space 11}{res}{c -(}trust:cdc_m{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 10}{res}{c -(}trust:pres_m{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 9}{res}{c -(}trust:state_m{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 8}{res}{c -(}trust:expert_m{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 4}{res}{c -(}trust:health_frame{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 9}{res}{c -(}trust:shelter{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 9}{res}{c -(}trust:jobloss{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 5}{res}{c -(}trust:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 4}{res}{c -(}trust:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 3}{res}{c -(}trust:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 2}{res}{c -(}trust:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 11}{res}{c -(}trust:white{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 7}{res}{c -(}trust:education{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 13}{res}{c -(}trust:gop{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 5}{res}{c -(}trust:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 7}{res}{c -(}trust:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 6}{res}{c -(}trust:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 5}{res}{c -(}trust:state_ideol{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 4}{res}{c -(}trust:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 35}(1){p_end}
{p 0 27}{space 3}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_trust.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,346
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4305
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .001454
{col 65}{txt}avg ={col 71}{res}    .01421
{txt}Log marginal-likelihood = {res}-1631.0961{col 65}{txt}max ={col 71}{res}     .1062
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}trust          {txt}{c |}
{space 9}cdc_m {c |}{col 16}{res}{space 1}-.9717395{col 27}{space 2} .5361123{col 38}{space 2} .031015{col 48}{space 2}-.9622543{col 59}{space 2}-2.033648{col 70}{space 2} .0637181
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-.7528286{col 27}{space 2} .5269972{col 38}{space 2}  .03399{col 48}{space 2}-.7422687{col 59}{space 2} -1.80225{col 70}{space 2} .2550336
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}   -.6355{col 27}{space 2} .5171833{col 38}{space 2} .031816{col 48}{space 2}-.6355529{col 59}{space 2}-1.632963{col 70}{space 2} .3906162
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1} -.132243{col 27}{space 2}  .521316{col 38}{space 2} .034997{col 48}{space 2}-.1152621{col 59}{space 2} -1.19846{col 70}{space 2} .8419863
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1}-.6631034{col 27}{space 2} .2305794{col 38}{space 2} .008177{col 48}{space 2}-.6636026{col 59}{space 2}-1.113171{col 70}{space 2}-.2088355
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .3292735{col 27}{space 2} .1286287{col 38}{space 2} .003089{col 48}{space 2} .3289637{col 59}{space 2} .0770585{col 70}{space 2} .5805174
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .1280837{col 27}{space 2} .1134156{col 38}{space 2} .001213{col 48}{space 2} .1283096{col 59}{space 2}-.0952482{col 70}{space 2} .3521543
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1} 1.275692{col 27}{space 2} .3366405{col 38}{space 2} .009933{col 48}{space 2} 1.275879{col 59}{space 2} .6147116{col 70}{space 2} 1.937543
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} 1.063248{col 27}{space 2} .3250273{col 38}{space 2} .009742{col 48}{space 2} 1.061501{col 59}{space 2} .4279837{col 70}{space 2} 1.698831
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1} .7262809{col 27}{space 2} .3312454{col 38}{space 2} .009727{col 48}{space 2} .7266133{col 59}{space 2} .0790263{col 70}{space 2} 1.374457
{txt}expert_frame_h {c |}{col 16}{res}{space 1} .6751238{col 27}{space 2} .3280979{col 38}{space 2} .009214{col 48}{space 2} .6774635{col 59}{space 2} .0312267{col 70}{space 2} 1.312906
{txt}{space 9}white {c |}{col 16}{res}{space 1}-.0275046{col 27}{space 2} .1309632{col 38}{space 2} .003262{col 48}{space 2} -.028069{col 59}{space 2}-.2813702{col 70}{space 2} .2294056
{txt}{space 5}education {c |}{col 16}{res}{space 1} .1290168{col 27}{space 2} .0365076{col 38}{space 2} .000917{col 48}{space 2} .1293203{col 59}{space 2} .0565447{col 70}{space 2} .1994728
{txt}{space 11}gop {c |}{col 16}{res}{space 1} .3048588{col 27}{space 2} .1141431{col 38}{space 2} .001108{col 48}{space 2}  .303676{col 59}{space 2} .0819049{col 70}{space 2} .5277418
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0108032{col 27}{space 2} .0044288{col 38}{space 2} .000327{col 48}{space 2} .0109788{col 59}{space 2}  .001818{col 70}{space 2} .0191401
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0095376{col 27}{space 2} .0067947{col 38}{space 2} .000384{col 48}{space 2} .0094732{col 59}{space 2}-.0035412{col 70}{space 2} .0232343
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0026469{col 27}{space 2} .0066366{col 38}{space 2} .000418{col 48}{space 2} .0024641{col 59}{space 2}-.0098335{col 70}{space 2} .0160454
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0038634{col 27}{space 2}  .006622{col 38}{space 2} .000392{col 48}{space 2} .0038264{col 59}{space 2}-.0092569{col 70}{space 2} .0167292
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0002997{col 27}{space 2} .0065957{col 38}{space 2} .000454{col 48}{space 2} .0001065{col 59}{space 2} -.012223{col 70}{space 2} .0136806
{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 10}cut1 {c |}{col 16}{res}{space 1}-3.061213{col 27}{space 2} .3981053{col 38}{space 2} .033014{col 48}{space 2}  -3.0606{col 59}{space 2}-3.857294{col 70}{space 2}-2.305611
{txt}{space 10}cut2 {c |}{col 16}{res}{space 1}-1.892925{col 27}{space 2} .3826235{col 38}{space 2} .029013{col 48}{space 2}-1.890645{col 59}{space 2}-2.651268{col 70}{space 2} -1.14324
{txt}{space 10}cut3 {c |}{col 16}{res}{space 1}-.2607005{col 27}{space 2} .3793989{col 38}{space 2}  .03054{col 48}{space 2} -.237454{col 59}{space 2} -1.04114{col 70}{space 2} .4711699
{txt}{space 10}cut4 {c |}{col 16}{res}{space 1} 1.849774{col 27}{space 2} .3733826{col 38}{space 2} .029188{col 48}{space 2} 1.867845{col 59}{space 2} 1.078294{col 70}{space 2} 2.559756
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for some model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayestest interval {c -(}trust: cdc_m{c )-}+{c -(}trust:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:cdc_m{c )-}+{c -(}trust:cdc_frame_h {c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .71091{col 25}{space 2}   0.45334{col 37}{space 2} .0184838
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust: pres_m{c )-}+{c -(}trust:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:pres_m{c )-}+{c -(}trust:pres_frame _h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .72744{col 25}{space 2}   0.44528{col 37}{space 2} .0205552
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust: state_m{c )-}+{c -(}trust:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:state_m{c )-}+{c -(}trust:state_fra me_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .57029{col 25}{space 2}   0.49504{col 37}{space 2} .0209562
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust: expert_m{c )-}+{c -(}trust:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:expert_m{c )-}+{c -(}trust:expert_f rame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .85697{col 25}{space 2}   0.35011{col 37}{space 2} .0167741
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust:health_frame{c )-}+{c -(}trust:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:health_frame{c )-}+{c -(}trust:cdc_ frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99483{col 25}{space 2}   0.07172{col 37}{space 2} .0004681
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust:health_frame{c )-}+{c -(}trust:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:health_frame{c )-}+{c -(}trust:pres _frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .96217{col 25}{space 2}   0.19079{col 37}{space 2} .0013053
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust:health_frame{c )-}+{c -(}trust:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:health_frame{c )-}+{c -(}trust:stat e_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .60503{col 25}{space 2}   0.48885{col 37}{space 2}  .004218
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}trust:health_frame{c )-}+{c -(}trust:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}trust:health_frame{c )-}+{c -(}trust:expe rt_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .52054{col 25}{space 2}   0.49958{col 37}{space 2}  .004878
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. bayes, block({c -(}knowledge: cdc_m{c )-}) block({c -(}knowledge: pres_m{c )-}) block({c -(}knowledge: state_m{c )-}) block({c -(}knowledge: expert_m{c )-}) block({c -(}knowledge: health_frame{c )-}) block({c -(}knowledge: shelter{c )-}) block({c -(}knowledge: jobloss{c )-}) block({c -(}knowledge: cdc_frame_h{c )-}) block({c -(}knowledge: pres_frame_h{c )-}) block({c -(}knowledge: state_frame_h{c )-}) block({c -(}knowledge: expert_frame_h{c )-}) block({c -(}knowledge: white{c )-}) block({c -(}knowledge: education{c )-}) block({c -(}knowledge: gop{c )-}) block({c -(}knowledge: ideology_rs{c )-}) block({c -(}knowledge: cdc_ideol{c )-}) block({c -(}knowledge: pres_ideol{c )-}) block({c -(}knowledge: state_ideol{c )-}) block({c -(}knowledge: expert_ideol{c )-}) mcmcsize(100000) burnin(10000) saving(simdata2) rseed(32306) prior({c -(}knowledge:{c )-}, uniform(-10,10)): ologit knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 14}{space 2}{res:knowledge} ~ ologit(xb_knowledge,{res}{c -(}cut1{c )-}{txt} {res}{c -(}cut2{c )-}{txt} {res}{c -(}cut3{c )-}{txt} {res}{c -(}cut4{c )-}{txt}){p_end}

Priors: 
{p 0 31}{space 11}{res}{c -(}knowledge:cdc_m{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 10}{res}{c -(}knowledge:pres_m{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 9}{res}{c -(}knowledge:state_m{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 8}{res}{c -(}knowledge:expert_m{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 4}{res}{c -(}knowledge:health_frame{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 9}{res}{c -(}knowledge:shelter{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 9}{res}{c -(}knowledge:jobloss{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 5}{res}{c -(}knowledge:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 4}{res}{c -(}knowledge:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 3}{res}{c -(}knowledge:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 2}{res}{c -(}knowledge:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 11}{res}{c -(}knowledge:white{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 7}{res}{c -(}knowledge:education{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 13}{res}{c -(}knowledge:gop{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 5}{res}{c -(}knowledge:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 7}{res}{c -(}knowledge:cdc_ideol{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 6}{res}{c -(}knowledge:pres_ideol{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 5}{res}{c -(}knowledge:state_ideol{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 4}{res}{c -(}knowledge:expert_ideol{c )-}{txt} ~ uniform(-10,10){space 31}(1){p_end}
{p 0 31}{space 7}{res}{c -(}cut1 cut2 cut3 cut4{c )-}{txt} ~ 1 (flat){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_knowledge.
{p_end}

{res}{txt}Bayesian ordered logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   110,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    10,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   100,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,346
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4275
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .001644
{col 65}{txt}avg ={col 71}{res}    .01775
{txt}Log marginal-likelihood = {res}-1562.8771{col 65}{txt}max ={col 71}{res}     .1049
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}knowledge      {txt}{c |}
{space 9}cdc_m {c |}{col 16}{res}{space 1}-1.111977{col 27}{space 2} .5606155{col 38}{space 2} .034045{col 48}{space 2}-1.106874{col 59}{space 2}-2.207701{col 70}{space 2} .0080661
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-1.110284{col 27}{space 2} .5120694{col 38}{space 2} .032846{col 48}{space 2}-1.102458{col 59}{space 2}-2.132755{col 70}{space 2}-.1215481
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.8216379{col 27}{space 2} .5043965{col 38}{space 2} .031142{col 48}{space 2}-.8095969{col 59}{space 2}-1.825665{col 70}{space 2} .1746725
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.1665057{col 27}{space 2} .5051464{col 38}{space 2} .033225{col 48}{space 2}-.1616969{col 59}{space 2}-1.188932{col 70}{space 2} .8130551
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1}-.6150333{col 27}{space 2}  .231171{col 38}{space 2} .006873{col 48}{space 2}-.6138877{col 59}{space 2}-1.074071{col 70}{space 2}-.1657222
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .2742488{col 27}{space 2} .1292781{col 38}{space 2} .002384{col 48}{space 2} .2734923{col 59}{space 2} .0218495{col 70}{space 2} .5277798
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .1358199{col 27}{space 2} .1143968{col 38}{space 2} .001118{col 48}{space 2} .1362533{col 59}{space 2}-.0884333{col 70}{space 2} .3599961
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1} 1.391856{col 27}{space 2}  .341782{col 38}{space 2} .008603{col 48}{space 2} 1.393191{col 59}{space 2} .7219626{col 70}{space 2} 2.065058
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} 1.157866{col 27}{space 2} .3278304{col 38}{space 2} .008126{col 48}{space 2} 1.155938{col 59}{space 2} .5159638{col 70}{space 2} 1.801187
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1} .5888277{col 27}{space 2} .3306766{col 38}{space 2} .007881{col 48}{space 2} .5860328{col 59}{space 2}-.0578976{col 70}{space 2} 1.244866
{txt}expert_frame_h {c |}{col 16}{res}{space 1} .6542509{col 27}{space 2}  .327856{col 38}{space 2} .008015{col 48}{space 2} .6548101{col 59}{space 2} .0115106{col 70}{space 2}  1.29336
{txt}{space 9}white {c |}{col 16}{res}{space 1}-.1328908{col 27}{space 2} .1323841{col 38}{space 2} .002309{col 48}{space 2}-.1324731{col 59}{space 2}-.3928856{col 70}{space 2} .1260214
{txt}{space 5}education {c |}{col 16}{res}{space 1}  .133133{col 27}{space 2} .0368887{col 38}{space 2} .000677{col 48}{space 2} .1332794{col 59}{space 2} .0609286{col 70}{space 2} .2059144
{txt}{space 11}gop {c |}{col 16}{res}{space 1} .3373461{col 27}{space 2} .1157521{col 38}{space 2}  .00113{col 48}{space 2} .3361169{col 59}{space 2} .1140777{col 70}{space 2} .5647351
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0095711{col 27}{space 2} .0046236{col 38}{space 2} .000354{col 48}{space 2} .0097916{col 59}{space 2} .0002157{col 70}{space 2}  .018356
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 1} .0116082{col 27}{space 2} .0071415{col 38}{space 2} .000434{col 48}{space 2} .0115144{col 59}{space 2}-.0024723{col 70}{space 2} .0256155
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 1} .0081897{col 27}{space 2}  .006561{col 38}{space 2}  .00042{col 48}{space 2} .0080681{col 59}{space 2}-.0045606{col 70}{space 2} .0212393
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 1} .0058127{col 27}{space 2} .0065797{col 38}{space 2} .000406{col 48}{space 2} .0056587{col 59}{space 2}-.0068871{col 70}{space 2} .0191245
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 1} .0030838{col 27}{space 2} .0064948{col 38}{space 2} .000437{col 48}{space 2} .0030449{col 59}{space 2}-.0092846{col 70}{space 2} .0162034
{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 10}cut1 {c |}{col 16}{res}{space 1}-3.463862{col 27}{space 2} .4396947{col 38}{space 2}  .03014{col 48}{space 2}-3.458772{col 59}{space 2}-4.328094{col 70}{space 2}-2.622256
{txt}{space 10}cut2 {c |}{col 16}{res}{space 1}-2.401743{col 27}{space 2} .3881487{col 38}{space 2} .029469{col 48}{space 2}-2.387297{col 59}{space 2}-3.193752{col 70}{space 2}-1.665461
{txt}{space 10}cut3 {c |}{col 16}{res}{space 1}-.5210678{col 27}{space 2} .3762651{col 38}{space 2} .028967{col 48}{space 2}-.5161379{col 59}{space 2} -1.30534{col 70}{space 2} .1836587
{txt}{space 10}cut4 {c |}{col 16}{res}{space 1} 1.697297{col 27}{space 2} .3704043{col 38}{space 2} .028891{col 48}{space 2} 1.713265{col 59}{space 2} .9171892{col 70}{space 2} 2.370629
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{p 0 6 2}Note: {help j_bayes_defaultpriors:Default priors} are used for some model parameters.{p_end}
{res}{txt}{p 0 6 0 80}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayestest interval {c -(}knowledge: cdc_m{c )-}+{c -(}knowledge:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}knowledge:cdc_m{c )-}+{c -(}knowledge:cdc _frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .68691{col 25}{space 2}   0.46375{col 37}{space 2} .0207117
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}knowledge:health_frame{c )-}+{c -(}knowledge:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}knowledge:health_frame{c )-}+{c -(}knowle dge:cdc_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99916{col 25}{space 2}   0.02897{col 37}{space 2} .0002284
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}knowledge:health_frame{c )-}+{c -(}knowledge:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}knowledge:health_frame{c )-}+{c -(}knowle dge:pres_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99073{col 25}{space 2}   0.09583{col 37}{space 2} .0005821
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}knowledge:health_frame{c )-}+{c -(}knowledge:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}knowledge:health_frame{c )-}+{c -(}knowle dge:state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .45325{col 25}{space 2}   0.49781{col 37}{space 2} .0041109
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}knowledge:health_frame{c )-}+{c -(}knowledge:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   100,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}knowledge:health_frame{c )-}+{c -(}knowle dge:expert_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .56866{col 25}{space 2}   0.49527{col 37}{space 2} .0045364
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. sum no_shop trust knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}      1,348    .7403561    .4386019          0          1
{txt}{space 7}trust {c |}{res}      1,346    4.131501     .869014          1          5
{txt}{space 3}knowledge {c |}{res}      1,346     4.17162    .8240601          1          5
{txt}{space 7}cdc_m {c |}{res}      1,348    .1913947     .393545          0          1
{txt}{space 6}pres_m {c |}{res}      1,348    .2114243    .4084701          0          1
{txt}{hline 13}{c +}{hline 57}
{space 5}state_m {c |}{res}      1,348    .1913947     .393545          0          1
{txt}{space 4}expert_m {c |}{res}      1,348    .2040059    .4031229          0          1
{txt}health_frame {c |}{res}      1,348    .4651335     .498968          0          1
{txt}{space 5}shelter {c |}{res}      1,348    .7908012    .4068876          0          1
{txt}{space 5}jobloss {c |}{res}      1,348    .3048961    .4605343          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}cdc_frame_h {c |}{res}      1,348    .0919881    .2891165          0          1
{txt}pres_frame_h {c |}{res}      1,348    .0994065    .2993181          0          1
{txt}state_fram~h {c |}{res}      1,348    .0882789    .2838054          0          1
{txt}expert_fra~h {c |}{res}      1,348    .0949555    .2932622          0          1
{txt}{space 7}white {c |}{res}      1,348    .7863501    .4100345          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}education {c |}{res}      1,348    2.999258    1.401823          1          5
{txt}{space 9}gop {c |}{res}      1,348    .3227003    .4676827          0          1
{txt}{space 1}ideology_rs {c |}{res}      1,348    71.97494    24.68483          0        100
{txt}{space 3}cdc_ideol {c |}{res}      1,348    14.21035    30.92887          0        100
{txt}{space 2}pres_ideol {c |}{res}      1,348    15.43027    31.95092          0        100
{txt}{hline 13}{c +}{hline 57}
{space 1}state_ideol {c |}{res}      1,348    13.69106    30.16558          0        100
{txt}expert_ideol {c |}{res}      1,348    14.51533    30.69289          0        100

{com}. sum econ_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}econ_frame {c |}{res}      1,348    .5333828    .4990695          0          1

{com}. sum gender

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}gender {c |}{res}      1,348    .4695846    .4992593          0          1

{com}. tab ethics
{err}variable {bf}ethics{sf} not found
{txt}{search r(111), local:r(111);}

{com}. tab ethnic

     {txt}ethnic {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,060       78.64       78.64
{txt}          2 {c |}{res}        166       12.31       90.95
{txt}          3 {c |}{res}         18        1.34       92.28
{txt}          4 {c |}{res}         69        5.12       97.40
{txt}          5 {c |}{res}          1        0.07       97.48
{txt}          6 {c |}{res}         34        2.52      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. ologit distance trust knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1251.7591}  
Iteration 2:{space 3}log likelihood = {res:-1250.5568}  
Iteration 3:{space 3}log likelihood = {res:-1250.5559}  
Iteration 4:{space 3}log likelihood = {res:-1250.5559}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}21{txt}){col 67}= {res}    145.57
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1250.5559{txt}{col 49}Pseudo R2{col 67}= {res}    0.0550

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}trust {c |}{col 16}{res}{space 2} .4249092{col 28}{space 2} .1000335{col 39}{space 1}    4.25{col 48}{space 3}0.000{col 56}{space 4} .2288472{col 69}{space 3} .6209713
{txt}{space 5}knowledge {c |}{col 16}{res}{space 2} .2793662{col 28}{space 2} .1064279{col 39}{space 1}    2.62{col 48}{space 3}0.009{col 56}{space 4} .0707713{col 69}{space 3} .4879612
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2} .4262321{col 28}{space 2} .5893643{col 39}{space 1}    0.72{col 48}{space 3}0.470{col 56}{space 4}-.7289007{col 69}{space 3} 1.581365
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1836846{col 28}{space 2} .5410726{col 39}{space 1}   -0.34{col 48}{space 3}0.734{col 56}{space 4}-1.244167{col 69}{space 3} .8767982
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.7517049{col 28}{space 2}  .544029{col 39}{space 1}   -1.38{col 48}{space 3}0.167{col 56}{space 4}-1.817982{col 69}{space 3} .3145724
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .7978122{col 28}{space 2} .5637644{col 39}{space 1}    1.42{col 48}{space 3}0.157{col 56}{space 4}-.3071458{col 69}{space 3}  1.90277
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .2903896{col 28}{space 2} .2610373{col 39}{space 1}    1.11{col 48}{space 3}0.266{col 56}{space 4}-.2212342{col 69}{space 3} .8020134
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}-.0378446{col 28}{space 2} .1406698{col 39}{space 1}   -0.27{col 48}{space 3}0.788{col 56}{space 4}-.3135523{col 69}{space 3} .2378632
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1817084{col 28}{space 2} .1276126{col 39}{space 1}    1.42{col 48}{space 3}0.154{col 56}{space 4}-.0684076{col 69}{space 3} .4318244
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} -.426338{col 28}{space 2} .3746759{col 39}{space 1}   -1.14{col 48}{space 3}0.255{col 56}{space 4}-1.160689{col 69}{space 3} .3080133
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2}-.0241561{col 28}{space 2} .3625514{col 39}{space 1}   -0.07{col 48}{space 3}0.947{col 56}{space 4}-.7347437{col 69}{space 3} .6864316
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.0064886{col 28}{space 2} .3715313{col 39}{space 1}   -0.02{col 48}{space 3}0.986{col 56}{space 4}-.7346765{col 69}{space 3} .7216993
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.4696248{col 28}{space 2} .3631924{col 39}{space 1}   -1.29{col 48}{space 3}0.196{col 56}{space 4}-1.181469{col 69}{space 3} .2422191
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4484661{col 28}{space 2} .1411534{col 39}{space 1}    3.18{col 48}{space 3}0.001{col 56}{space 4} .1718106{col 69}{space 3} .7251216
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0768115{col 28}{space 2} .0414324{col 39}{space 1}    1.85{col 48}{space 3}0.064{col 56}{space 4}-.0043944{col 69}{space 3} .1580174
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.2520402{col 28}{space 2} .1266999{col 39}{space 1}   -1.99{col 48}{space 3}0.047{col 56}{space 4}-.5003674{col 69}{space 3}-.0037131
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0084758{col 28}{space 2}  .004973{col 39}{space 1}    1.70{col 48}{space 3}0.088{col 56}{space 4}-.0012711{col 69}{space 3} .0182228
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2}-.0047386{col 28}{space 2} .0075342{col 39}{space 1}   -0.63{col 48}{space 3}0.529{col 56}{space 4}-.0195053{col 69}{space 3}  .010028
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0014057{col 28}{space 2} .0069891{col 39}{space 1}    0.20{col 48}{space 3}0.841{col 56}{space 4}-.0122927{col 69}{space 3} .0151041
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0098423{col 28}{space 2} .0072166{col 39}{space 1}    1.36{col 48}{space 3}0.173{col 56}{space 4} -.004302{col 69}{space 3} .0239866
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2}-.0088203{col 28}{space 2} .0072273{col 39}{space 1}   -1.22{col 48}{space 3}0.222{col 56}{space 4}-.0229855{col 69}{space 3} .0053449
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-.1292299{col 28}{space 2} .5273654{col 56}{space 4}-1.162847{col 69}{space 3} .9043874
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .9224697{col 28}{space 2} .5044844{col 56}{space 4}-.0663016{col 69}{space 3} 1.911241
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.859626{col 28}{space 2} .5000448{col 56}{space 4} .8795559{col 69}{space 3} 2.839695
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 3.515553{col 28}{space 2}  .506377{col 56}{space 4} 2.523073{col 69}{space 3} 4.508034
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance trust cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs cdc_ideol pres_ideol state_ideol expert_ideol

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1255.1402}  
Iteration 2:{space 3}log likelihood = {res:-1253.9876}  
Iteration 3:{space 3}log likelihood = {res:-1253.9868}  
Iteration 4:{space 3}log likelihood = {res:-1253.9868}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}20{txt}){col 67}= {res}    138.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1253.9868{txt}{col 49}Pseudo R2{col 67}= {res}    0.0524

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}trust {c |}{col 16}{res}{space 2} .6159705{col 28}{space 2} .0686939{col 39}{space 1}    8.97{col 48}{space 3}0.000{col 56}{space 4} .4813329{col 69}{space 3} .7506081
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2} .3385589{col 28}{space 2} .5878982{col 39}{space 1}    0.58{col 48}{space 3}0.565{col 56}{space 4}-.8137005{col 69}{space 3} 1.490818
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.2739738{col 28}{space 2}  .540641{col 39}{space 1}   -0.51{col 48}{space 3}0.612{col 56}{space 4}-1.333611{col 69}{space 3}  .785663
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.7675207{col 28}{space 2} .5451365{col 39}{space 1}   -1.41{col 48}{space 3}0.159{col 56}{space 4}-1.835968{col 69}{space 3} .3009272
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}  .785995{col 28}{space 2} .5627639{col 39}{space 1}    1.40{col 48}{space 3}0.163{col 56}{space 4}-.3170019{col 69}{space 3} 1.888992
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .2872179{col 28}{space 2} .2605327{col 39}{space 1}    1.10{col 48}{space 3}0.270{col 56}{space 4}-.2234168{col 69}{space 3} .7978526
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}-.0413156{col 28}{space 2} .1405573{col 39}{space 1}   -0.29{col 48}{space 3}0.769{col 56}{space 4}-.3168029{col 69}{space 3} .2341717
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1894132{col 28}{space 2} .1272687{col 39}{space 1}    1.49{col 48}{space 3}0.137{col 56}{space 4}-.0600289{col 69}{space 3} .4388553
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.3588234{col 28}{space 2} .3728361{col 39}{space 1}   -0.96{col 48}{space 3}0.336{col 56}{space 4}-1.089569{col 69}{space 3} .3719218
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .0157378{col 28}{space 2} .3612217{col 39}{space 1}    0.04{col 48}{space 3}0.965{col 56}{space 4}-.6922438{col 69}{space 3} .7237193
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.0273098{col 28}{space 2} .3711548{col 39}{space 1}   -0.07{col 48}{space 3}0.941{col 56}{space 4}-.7547599{col 69}{space 3} .7001403
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.4716106{col 28}{space 2} .3626611{col 39}{space 1}   -1.30{col 48}{space 3}0.193{col 56}{space 4}-1.182413{col 69}{space 3} .2391921
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4269744{col 28}{space 2}  .140742{col 39}{space 1}    3.03{col 48}{space 3}0.002{col 56}{space 4}  .151125{col 69}{space 3} .7028237
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0800117{col 28}{space 2} .0413964{col 39}{space 1}    1.93{col 48}{space 3}0.053{col 56}{space 4}-.0011239{col 69}{space 3} .1611472
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.2400885{col 28}{space 2} .1264816{col 39}{space 1}   -1.90{col 48}{space 3}0.058{col 56}{space 4}-.4879879{col 69}{space 3}  .007811
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0083533{col 28}{space 2} .0049582{col 39}{space 1}    1.68{col 48}{space 3}0.092{col 56}{space 4}-.0013645{col 69}{space 3} .0180711
{txt}{space 5}cdc_ideol {c |}{col 16}{res}{space 2}-.0036646{col 28}{space 2} .0075108{col 39}{space 1}   -0.49{col 48}{space 3}0.626{col 56}{space 4}-.0183856{col 69}{space 3} .0110563
{txt}{space 4}pres_ideol {c |}{col 16}{res}{space 2} .0025127{col 28}{space 2} .0069662{col 39}{space 1}    0.36{col 48}{space 3}0.718{col 56}{space 4}-.0111408{col 69}{space 3} .0161662
{txt}{space 3}state_ideol {c |}{col 16}{res}{space 2} .0101987{col 28}{space 2} .0072123{col 39}{space 1}    1.41{col 48}{space 3}0.157{col 56}{space 4}-.0039371{col 69}{space 3} .0243345
{txt}{space 2}expert_ideol {c |}{col 16}{res}{space 2}-.0081794{col 28}{space 2} .0072006{col 39}{space 1}   -1.14{col 48}{space 3}0.256{col 56}{space 4}-.0222923{col 69}{space 3} .0059336
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-.4942196{col 28}{space 2} .5074657{col 56}{space 4}-1.488834{col 69}{space 3}  .500395
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .5548828{col 28}{space 2} .4830023{col 56}{space 4}-.3917843{col 69}{space 3}  1.50155
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.492239{col 28}{space 2} .4783239{col 56}{space 4}  .554741{col 69}{space 3} 2.429736
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2}  3.14363{col 28}{space 2} .4841908{col 56}{space 4} 2.194634{col 69}{space 3} 4.092627
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance trust cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res: -1258.411}  
Iteration 2:{space 3}log likelihood = {res:-1257.4259}  
Iteration 3:{space 3}log likelihood = {res:-1257.4254}  
Iteration 4:{space 3}log likelihood = {res:-1257.4254}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}    131.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1257.4254{txt}{col 49}Pseudo R2{col 67}= {res}    0.0498

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}trust {c |}{col 16}{res}{space 2} .6172914{col 28}{space 2} .0685918{col 39}{space 1}    9.00{col 48}{space 3}0.000{col 56}{space 4}  .482854{col 69}{space 3} .7517289
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2}  .071944{col 28}{space 2} .2491288{col 39}{space 1}    0.29{col 48}{space 3}0.773{col 56}{space 4}-.4163395{col 69}{space 3} .5602275
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2} -.095464{col 28}{space 2} .2394237{col 39}{space 1}   -0.40{col 48}{space 3}0.690{col 56}{space 4}-.5647259{col 69}{space 3} .3737978
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.0784407{col 28}{space 2}  .243105{col 39}{space 1}   -0.32{col 48}{space 3}0.747{col 56}{space 4}-.5549179{col 69}{space 3} .3980364
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2060193{col 28}{space 2} .2473488{col 39}{space 1}    0.83{col 48}{space 3}0.405{col 56}{space 4}-.2787754{col 69}{space 3}  .690814
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .2872106{col 28}{space 2} .2605732{col 39}{space 1}    1.10{col 48}{space 3}0.270{col 56}{space 4}-.2235035{col 69}{space 3} .7979247
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}-.0470385{col 28}{space 2} .1401158{col 39}{space 1}   -0.34{col 48}{space 3}0.737{col 56}{space 4}-.3216603{col 69}{space 3} .2275834
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2}  .183381{col 28}{space 2} .1267728{col 39}{space 1}    1.45{col 48}{space 3}0.148{col 56}{space 4}-.0650891{col 69}{space 3}  .431851
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.3587873{col 28}{space 2} .3735488{col 39}{space 1}   -0.96{col 48}{space 3}0.337{col 56}{space 4}-1.090929{col 69}{space 3} .3733549
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .0105941{col 28}{space 2} .3608567{col 39}{space 1}    0.03{col 48}{space 3}0.977{col 56}{space 4}-.6966721{col 69}{space 3} .7178603
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.0224357{col 28}{space 2} .3687143{col 39}{space 1}   -0.06{col 48}{space 3}0.951{col 56}{space 4}-.7451025{col 69}{space 3} .7002312
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.4469612{col 28}{space 2} .3633169{col 39}{space 1}   -1.23{col 48}{space 3}0.219{col 56}{space 4}-1.159049{col 69}{space 3} .2651268
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4414555{col 28}{space 2}  .140384{col 39}{space 1}    3.14{col 48}{space 3}0.002{col 56}{space 4} .1663079{col 69}{space 3}  .716603
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0800335{col 28}{space 2} .0412686{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.0008515{col 69}{space 3} .1609185
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.2244151{col 28}{space 2} .1260965{col 39}{space 1}   -1.78{col 48}{space 3}0.075{col 56}{space 4}-.4715596{col 69}{space 3} .0227295
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0087173{col 28}{space 2}  .002363{col 39}{space 1}    3.69{col 48}{space 3}0.000{col 56}{space 4} .0040859{col 69}{space 3} .0133487
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-.4411008{col 28}{space 2} .4234844{col 56}{space 4}-1.271115{col 69}{space 3} .3889134
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .6059232{col 28}{space 2}  .393347{col 56}{space 4}-.1650227{col 69}{space 3} 1.376869
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.539215{col 28}{space 2} .3868174{col 56}{space 4}  .781067{col 69}{space 3} 2.297363
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 3.181756{col 28}{space 2} .3936941{col 56}{space 4} 2.410129{col 69}{space 3} 3.953382
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance trust knowledge cdc_m pres_m state_m expert_m health_frame shelter jobloss gender white education gop ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1240.7752}  
Iteration 2:{space 3}log likelihood = {res:-1239.1694}  
Iteration 3:{space 3}log likelihood = {res:-1239.1676}  
Iteration 4:{space 3}log likelihood = {res:-1239.1676}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}14{txt}){col 67}= {res}    168.35
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1239.1676{txt}{col 49}Pseudo R2{col 67}= {res}    0.0636

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trust {c |}{col 14}{res}{space 2} .4545155{col 26}{space 2} .0992932{col 37}{space 1}    4.58{col 46}{space 3}0.000{col 54}{space 4} .2599043{col 67}{space 3} .6491266
{txt}{space 3}knowledge {c |}{col 14}{res}{space 2} .2872124{col 26}{space 2} .1051628{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0810971{col 67}{space 3} .4933277
{txt}{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1410975{col 26}{space 2} .1879271{col 37}{space 1}   -0.75{col 46}{space 3}0.453{col 54}{space 4}-.5094279{col 67}{space 3}  .227233
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1306871{col 26}{space 2} .1808245{col 37}{space 1}   -0.72{col 46}{space 3}0.470{col 54}{space 4}-.4850967{col 67}{space 3} .2237225
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.1320973{col 26}{space 2} .1848767{col 37}{space 1}   -0.71{col 46}{space 3}0.475{col 54}{space 4}-.4944489{col 67}{space 3} .2302544
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0818964{col 26}{space 2} .1829613{col 37}{space 1}   -0.45{col 46}{space 3}0.654{col 54}{space 4}-.4404939{col 67}{space 3} .2767011
{txt}health_frame {c |}{col 14}{res}{space 2} .1239435{col 26}{space 2} .1168491{col 37}{space 1}    1.06{col 46}{space 3}0.289{col 54}{space 4}-.1050765{col 67}{space 3} .3529634
{txt}{space 5}shelter {c |}{col 14}{res}{space 2}-.0223263{col 26}{space 2} .1411445{col 37}{space 1}   -0.16{col 46}{space 3}0.874{col 54}{space 4}-.2989643{col 67}{space 3} .2543118
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2075387{col 26}{space 2} .1283337{col 37}{space 1}    1.62{col 46}{space 3}0.106{col 54}{space 4}-.0439907{col 67}{space 3} .4590681
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6786856{col 26}{space 2}  .118884{col 37}{space 1}   -5.71{col 46}{space 3}0.000{col 54}{space 4}-.9116939{col 67}{space 3}-.4456772
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4671035{col 26}{space 2} .1419873{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .1888135{col 67}{space 3} .7453934
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1171023{col 26}{space 2} .0419624{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .0348575{col 67}{space 3}  .199347
{txt}{space 9}gop {c |}{col 14}{res}{space 2} -.197799{col 26}{space 2}  .126905{col 37}{space 1}   -1.56{col 46}{space 3}0.119{col 54}{space 4}-.4465283{col 67}{space 3} .0509303
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2}  .007841{col 26}{space 2} .0023971{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0031428{col 67}{space 3} .0125392
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.3202433{col 26}{space 2} .4241161{col 54}{space 4}-1.151496{col 67}{space 3}  .511009
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}  .737624{col 26}{space 2} .3950941{col 54}{space 4}-.0367461{col 67}{space 3} 1.511994
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.678422{col 26}{space 2} .3889167{col 54}{space 4} .9161596{col 67}{space 3} 2.440685
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 3.352536{col 26}{space 2} .3965233{col 54}{space 4} 2.575364{col 67}{space 3} 4.129707
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance trust knowledge cdc_m pres_m state_m expert_m health_frame gop ideology_rs shelter jobloss gender white education age

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1240.6939}  
Iteration 2:{space 3}log likelihood = {res: -1239.079}  
Iteration 3:{space 3}log likelihood = {res:-1239.0771}  
Iteration 4:{space 3}log likelihood = {res:-1239.0771}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    168.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1239.0771{txt}{col 49}Pseudo R2{col 67}= {res}    0.0637

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}trust {c |}{col 14}{res}{space 2} .4539386{col 26}{space 2} .0992581{col 37}{space 1}    4.57{col 46}{space 3}0.000{col 54}{space 4} .2593963{col 67}{space 3}  .648481
{txt}{space 3}knowledge {c |}{col 14}{res}{space 2} .2863114{col 26}{space 2} .1051348{col 37}{space 1}    2.72{col 46}{space 3}0.006{col 54}{space 4}  .080251{col 67}{space 3} .4923719
{txt}{space 7}cdc_m {c |}{col 14}{res}{space 2}-.1382456{col 26}{space 2} .1880684{col 37}{space 1}   -0.74{col 46}{space 3}0.462{col 54}{space 4}-.5068529{col 67}{space 3} .2303617
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1277176{col 26}{space 2}  .180955{col 37}{space 1}   -0.71{col 46}{space 3}0.480{col 54}{space 4}-.4823829{col 67}{space 3} .2269478
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.1300026{col 26}{space 2} .1849267{col 37}{space 1}   -0.70{col 46}{space 3}0.482{col 54}{space 4}-.4924522{col 67}{space 3} .2324469
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0772977{col 26}{space 2} .1832758{col 37}{space 1}   -0.42{col 46}{space 3}0.673{col 54}{space 4}-.4365116{col 67}{space 3} .2819162
{txt}health_frame {c |}{col 14}{res}{space 2} .1247442{col 26}{space 2} .1168577{col 37}{space 1}    1.07{col 46}{space 3}0.286{col 54}{space 4}-.1042927{col 67}{space 3} .3537811
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.1997722{col 26}{space 2}  .127006{col 37}{space 1}   -1.57{col 46}{space 3}0.116{col 54}{space 4}-.4486993{col 67}{space 3}  .049155
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0078039{col 26}{space 2} .0023986{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} .0031027{col 67}{space 3} .0125051
{txt}{space 5}shelter {c |}{col 14}{res}{space 2}-.0207176{col 26}{space 2} .1411829{col 37}{space 1}   -0.15{col 46}{space 3}0.883{col 54}{space 4}-.2974309{col 67}{space 3} .2559957
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2164786{col 26}{space 2} .1300365{col 37}{space 1}    1.66{col 46}{space 3}0.096{col 54}{space 4}-.0383883{col 67}{space 3} .4713456
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.677424{col 26}{space 2} .1189294{col 37}{space 1}   -5.70{col 46}{space 3}0.000{col 54}{space 4}-.9105214{col 67}{space 3}-.4443265
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4552629{col 26}{space 2} .1446656{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4} .1717236{col 67}{space 3} .7388022
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1152632{col 26}{space 2} .0421961{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0325603{col 67}{space 3}  .197966
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0162755{col 26}{space 2} .0382728{col 37}{space 1}    0.43{col 46}{space 3}0.671{col 54}{space 4}-.0587377{col 67}{space 3} .0912887
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.2675612{col 26}{space 2} .4418232{col 54}{space 4}-1.133519{col 67}{space 3} .5983963
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .7901286{col 26}{space 2} .4139127{col 54}{space 4}-.0211254{col 67}{space 3} 1.601383
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.730842{col 26}{space 2} .4079692{col 54}{space 4} .9312371{col 67}{space 3} 2.530447
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 3.405286{col 26}{space 2} .4155074{col 54}{space 4} 2.590906{col 67}{space 3} 4.219666
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance trust knowledge cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h gop ideology_rs shelter jobloss gender white education age

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1239.2523}  
Iteration 2:{space 3}log likelihood = {res:-1237.5809}  
Iteration 3:{space 3}log likelihood = {res:-1237.5789}  
Iteration 4:{space 3}log likelihood = {res:-1237.5789}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}    171.52
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1237.5789{txt}{col 49}Pseudo R2{col 67}= {res}    0.0648

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}trust {c |}{col 16}{res}{space 2} .4547272{col 28}{space 2} .0995539{col 39}{space 1}    4.57{col 48}{space 3}0.000{col 56}{space 4} .2596051{col 69}{space 3} .6498494
{txt}{space 5}knowledge {c |}{col 16}{res}{space 2} .2911237{col 28}{space 2} .1057516{col 39}{space 1}    2.75{col 48}{space 3}0.006{col 56}{space 4} .0838543{col 69}{space 3} .4983931
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2} .0368377{col 28}{space 2} .2519605{col 39}{space 1}    0.15{col 48}{space 3}0.884{col 56}{space 4}-.4569958{col 69}{space 3} .5306713
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.0936112{col 28}{space 2} .2421024{col 39}{space 1}   -0.39{col 48}{space 3}0.699{col 56}{space 4}-.5681231{col 69}{space 3} .3809007
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.0866051{col 28}{space 2} .2452902{col 39}{space 1}   -0.35{col 48}{space 3}0.724{col 56}{space 4} -.567365{col 69}{space 3} .3941548
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}  .161618{col 28}{space 2}  .249966{col 39}{space 1}    0.65{col 48}{space 3}0.518{col 56}{space 4}-.3283064{col 69}{space 3} .6515423
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2}  .339512{col 28}{space 2} .2636559{col 39}{space 1}    1.29{col 48}{space 3}0.198{col 56}{space 4}-.1772441{col 69}{space 3} .8562681
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.3934887{col 28}{space 2} .3786329{col 39}{space 1}   -1.04{col 48}{space 3}0.299{col 56}{space 4}-1.135596{col 69}{space 3} .3486181
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2}-.0785871{col 28}{space 2} .3649081{col 39}{space 1}   -0.22{col 48}{space 3}0.829{col 56}{space 4}-.7937937{col 69}{space 3} .6366196
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.0954838{col 28}{space 2}  .372993{col 39}{space 1}   -0.26{col 48}{space 3}0.798{col 56}{space 4}-.8265366{col 69}{space 3}  .635569
{txt}expert_frame_h {c |}{col 16}{res}{space 2} -.515005{col 28}{space 2} .3664264{col 39}{space 1}   -1.41{col 48}{space 3}0.160{col 56}{space 4}-1.233188{col 69}{space 3} .2031776
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.2000079{col 28}{space 2} .1272814{col 39}{space 1}   -1.57{col 48}{space 3}0.116{col 56}{space 4}-.4494749{col 69}{space 3} .0494592
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0077472{col 28}{space 2}    .0024{col 39}{space 1}    3.23{col 48}{space 3}0.001{col 56}{space 4} .0030433{col 69}{space 3} .0124511
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}-.0285234{col 28}{space 2} .1415299{col 39}{space 1}   -0.20{col 48}{space 3}0.840{col 56}{space 4}-.3059168{col 69}{space 3}   .24887
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .2208382{col 28}{space 2} .1302414{col 39}{space 1}    1.70{col 48}{space 3}0.090{col 56}{space 4}-.0344302{col 69}{space 3} .4761067
{txt}{space 8}gender {c |}{col 16}{res}{space 2} -.676822{col 28}{space 2} .1192728{col 39}{space 1}   -5.67{col 48}{space 3}0.000{col 56}{space 4}-.9105923{col 69}{space 3}-.4430516
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4585409{col 28}{space 2} .1450835{col 39}{space 1}    3.16{col 48}{space 3}0.002{col 56}{space 4} .1741825{col 69}{space 3} .7428993
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1123456{col 28}{space 2} .0422601{col 39}{space 1}    2.66{col 48}{space 3}0.008{col 56}{space 4} .0295173{col 69}{space 3}  .195174
{txt}{space 11}age {c |}{col 16}{res}{space 2} .0163614{col 28}{space 2} .0383531{col 39}{space 1}    0.43{col 48}{space 3}0.670{col 56}{space 4}-.0588093{col 69}{space 3} .0915321
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-.1670442{col 28}{space 2} .4605864{col 56}{space 4}-1.069777{col 69}{space 3} .7356885
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .8906454{col 28}{space 2} .4339056{col 56}{space 4} .0402061{col 69}{space 3} 1.741085
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.831652{col 28}{space 2} .4281907{col 56}{space 4}  .992414{col 69}{space 3} 2.670891
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 3.509273{col 28}{space 2} .4357721{col 56}{space 4} 2.655176{col 69}{space 3} 4.363371
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. gen ind = party

. tab party

      {txt}party {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        537       39.84       39.84
{txt}          2 {c |}{res}        435       32.27       72.11
{txt}          3 {c |}{res}        255       18.92       91.02
{txt}          4 {c |}{res}         22        1.63       92.66
{txt}          5 {c |}{res}         75        5.56       98.22
{txt}          6 {c |}{res}         24        1.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,348      100.00

{com}. recode ind 1=0 2=0 3=1 4=1 5=1 6=1
{txt}(ind: 1348 changes made)

{com}. sum ind

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}ind {c |}{res}      1,348    .2789318    .4486403          0          1

{com}. ologit distance trust knowledge cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h gop ind ideology_rs shelter jobloss gender white education age

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1237.5946}  
Iteration 2:{space 3}log likelihood = {res:-1235.8307}  
Iteration 3:{space 3}log likelihood = {res:-1235.8283}  
Iteration 4:{space 3}log likelihood = {res:-1235.8283}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}20{txt}){col 67}= {res}    175.02
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1235.8283{txt}{col 49}Pseudo R2{col 67}= {res}    0.0661

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}trust {c |}{col 16}{res}{space 2}  .453628{col 28}{space 2} .0996324{col 39}{space 1}    4.55{col 48}{space 3}0.000{col 56}{space 4}  .258352{col 69}{space 3}  .648904
{txt}{space 5}knowledge {c |}{col 16}{res}{space 2} .2812733{col 28}{space 2} .1058722{col 39}{space 1}    2.66{col 48}{space 3}0.008{col 56}{space 4} .0737676{col 69}{space 3} .4887791
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2} .0459913{col 28}{space 2} .2520953{col 39}{space 1}    0.18{col 48}{space 3}0.855{col 56}{space 4}-.4481065{col 69}{space 3} .5400891
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1012041{col 28}{space 2} .2420244{col 39}{space 1}   -0.42{col 48}{space 3}0.676{col 56}{space 4}-.5755632{col 69}{space 3}  .373155
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.0701466{col 28}{space 2} .2457279{col 39}{space 1}   -0.29{col 48}{space 3}0.775{col 56}{space 4}-.5517644{col 69}{space 3} .4114713
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1455607{col 28}{space 2} .2503468{col 39}{space 1}    0.58{col 48}{space 3}0.561{col 56}{space 4}-.3451101{col 69}{space 3} .6362315
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .3405672{col 28}{space 2} .2637101{col 39}{space 1}    1.29{col 48}{space 3}0.197{col 56}{space 4} -.176295{col 69}{space 3} .8574295
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.3941112{col 28}{space 2} .3790338{col 39}{space 1}   -1.04{col 48}{space 3}0.298{col 56}{space 4}-1.137004{col 69}{space 3} .3487814
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2}-.0730204{col 28}{space 2} .3647564{col 39}{space 1}   -0.20{col 48}{space 3}0.841{col 56}{space 4}-.7879298{col 69}{space 3}  .641889
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.1244624{col 28}{space 2}  .373591{col 39}{space 1}   -0.33{col 48}{space 3}0.739{col 56}{space 4}-.8566873{col 69}{space 3} .6077625
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.5043555{col 28}{space 2} .3669073{col 39}{space 1}   -1.37{col 48}{space 3}0.169{col 56}{space 4}-1.223481{col 69}{space 3} .2147696
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.3262678{col 28}{space 2} .1448312{col 39}{space 1}   -2.25{col 48}{space 3}0.024{col 56}{space 4}-.6101317{col 69}{space 3}-.0424039
{txt}{space 11}ind {c |}{col 16}{res}{space 2}-.2725995{col 28}{space 2}  .145538{col 39}{space 1}   -1.87{col 48}{space 3}0.061{col 56}{space 4}-.5578487{col 69}{space 3} .0126497
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0075876{col 28}{space 2}  .002403{col 39}{space 1}    3.16{col 48}{space 3}0.002{col 56}{space 4} .0028777{col 69}{space 3} .0122974
{txt}{space 7}shelter {c |}{col 16}{res}{space 2}-.0392859{col 28}{space 2} .1416148{col 39}{space 1}   -0.28{col 48}{space 3}0.781{col 56}{space 4}-.3168459{col 69}{space 3} .2382741
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .2107362{col 28}{space 2}  .130391{col 39}{space 1}    1.62{col 48}{space 3}0.106{col 56}{space 4}-.0448254{col 69}{space 3} .4662977
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6676692{col 28}{space 2} .1194737{col 39}{space 1}   -5.59{col 48}{space 3}0.000{col 56}{space 4}-.9018334{col 69}{space 3} -.433505
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4939695{col 28}{space 2} .1464975{col 39}{space 1}    3.37{col 48}{space 3}0.001{col 56}{space 4} .2068397{col 69}{space 3} .7810992
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .105515{col 28}{space 2} .0424725{col 39}{space 1}    2.48{col 48}{space 3}0.013{col 56}{space 4} .0222705{col 69}{space 3} .1887595
{txt}{space 11}age {c |}{col 16}{res}{space 2} .0135086{col 28}{space 2}  .038453{col 39}{space 1}    0.35{col 48}{space 3}0.725{col 56}{space 4} -.061858{col 69}{space 3} .0888752
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2} -.359593{col 28}{space 2} .4718111{col 56}{space 4}-1.284326{col 69}{space 3} .5651398
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2} .6982945{col 28}{space 2} .4458107{col 56}{space 4}-.1754785{col 69}{space 3} 1.572068
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} 1.639562{col 28}{space 2} .4401582{col 56}{space 4} .7768675{col 69}{space 3} 2.502256
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 3.320062{col 28}{space 2} .4470413{col 56}{space 4} 2.443877{col 69}{space 3} 4.196246
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1.dta saved

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 4 Apr 2020, 13:19:06
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Apr 2020, 12:35:31

{com}. use "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study14R.dta"

. set more off

. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop gender

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-737.14992}  
Iteration 2:{space 3}log likelihood = {res:-736.61394}  
Iteration 3:{space 3}log likelihood = {res:-736.61354}  
Iteration 4:{space 3}log likelihood = {res:-736.61354}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     65.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-736.61354{txt}{col 49}Pseudo R2{col 67}= {res}    0.0425

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .2411935{col 28}{space 2} .2662675{col 39}{space 1}    0.91{col 48}{space 3}0.365{col 56}{space 4}-.2806812{col 69}{space 3} .7630682
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1572776{col 28}{space 2} .2488636{col 39}{space 1}   -0.63{col 48}{space 3}0.527{col 56}{space 4}-.6450414{col 69}{space 3} .3304861
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .0816037{col 28}{space 2} .2588895{col 39}{space 1}    0.32{col 48}{space 3}0.753{col 56}{space 4}-.4258104{col 69}{space 3} .5890177
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1889724{col 28}{space 2} .2590738{col 39}{space 1}    0.73{col 48}{space 3}0.466{col 56}{space 4}-.3188029{col 69}{space 3} .6967476
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8234339{col 28}{space 2} .2980313{col 39}{space 1}    2.76{col 48}{space 3}0.006{col 56}{space 4} .2393033{col 69}{space 3} 1.407565
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0824021{col 28}{space 2} .1571033{col 39}{space 1}    0.52{col 48}{space 3}0.600{col 56}{space 4}-.2255147{col 69}{space 3} .3903189
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .0012473{col 28}{space 2} .1390257{col 39}{space 1}    0.01{col 48}{space 3}0.993{col 56}{space 4}-.2712382{col 69}{space 3} .2737327
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2057394{col 28}{space 2} .4315345{col 39}{space 1}   -0.48{col 48}{space 3}0.634{col 56}{space 4}-1.051531{col 69}{space 3} .6400526
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1537936{col 28}{space 2} .4148167{col 39}{space 1}    0.37{col 48}{space 3}0.711{col 56}{space 4}-.6592323{col 69}{space 3} .9668195
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3693437{col 28}{space 2} .4195308{col 39}{space 1}   -0.88{col 48}{space 3}0.379{col 56}{space 4}-1.191609{col 69}{space 3} .4529216
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.8038448{col 28}{space 2} .4052236{col 39}{space 1}   -1.98{col 48}{space 3}0.047{col 56}{space 4}-1.598069{col 69}{space 3} -.009621
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4024987{col 28}{space 2} .1545046{col 39}{space 1}    2.61{col 48}{space 3}0.009{col 56}{space 4} .0996751{col 69}{space 3} .7053222
{txt}{space 5}education {c |}{col 16}{res}{space 2} .0687317{col 28}{space 2} .0461785{col 39}{space 1}    1.49{col 48}{space 3}0.137{col 56}{space 4}-.0217766{col 69}{space 3}   .15924
{txt}{space 11}gop {c |}{col 16}{res}{space 2}  -.06807{col 28}{space 2} .1411847{col 39}{space 1}   -0.48{col 48}{space 3}0.630{col 56}{space 4} -.344787{col 69}{space 3}  .208647
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6776219{col 28}{space 2}   .13075{col 39}{space 1}   -5.18{col 48}{space 3}0.000{col 56}{space 4}-.9338872{col 69}{space 3}-.4213567
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  .529819{col 28}{space 2} .2738305{col 39}{space 1}    1.93{col 48}{space 3}0.053{col 56}{space 4}-.0068789{col 69}{space 3} 1.066517
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.82287}  
Iteration 2:{space 3}log likelihood = {res:-732.14831}  
Iteration 3:{space 3}log likelihood = {res:-732.14766}  
Iteration 4:{space 3}log likelihood = {res:-732.14766}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     74.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.14766{txt}{col 49}Pseudo R2{col 67}= {res}    0.0483

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .2182732{col 28}{space 2}  .267345{col 39}{space 1}    0.82{col 48}{space 3}0.414{col 56}{space 4}-.3057134{col 69}{space 3} .7422598
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1855399{col 28}{space 2} .2499183{col 39}{space 1}   -0.74{col 48}{space 3}0.458{col 56}{space 4}-.6753708{col 69}{space 3}  .304291
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .0808928{col 28}{space 2} .2600986{col 39}{space 1}    0.31{col 48}{space 3}0.756{col 56}{space 4}-.4288911{col 69}{space 3} .5906768
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1690829{col 28}{space 2} .2600843{col 39}{space 1}    0.65{col 48}{space 3}0.516{col 56}{space 4} -.340673{col 69}{space 3} .6788387
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8441709{col 28}{space 2} .2998053{col 39}{space 1}    2.82{col 48}{space 3}0.005{col 56}{space 4} .2565633{col 69}{space 3} 1.431778
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0461846{col 28}{space 2} .1582116{col 39}{space 1}    0.29{col 48}{space 3}0.770{col 56}{space 4}-.2639045{col 69}{space 3} .3562737
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .0332535{col 28}{space 2} .1400279{col 39}{space 1}    0.24{col 48}{space 3}0.812{col 56}{space 4}-.2411961{col 69}{space 3} .3077031
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2408493{col 28}{space 2} .4332957{col 39}{space 1}   -0.56{col 48}{space 3}0.578{col 56}{space 4}-1.090093{col 69}{space 3} .6083946
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1484537{col 28}{space 2} .4164738{col 39}{space 1}    0.36{col 48}{space 3}0.722{col 56}{space 4}  -.66782{col 69}{space 3} .9647275
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3990044{col 28}{space 2} .4216742{col 39}{space 1}   -0.95{col 48}{space 3}0.344{col 56}{space 4}-1.225471{col 69}{space 3}  .427462
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7915489{col 28}{space 2} .4071213{col 39}{space 1}   -1.94{col 48}{space 3}0.052{col 56}{space 4}-1.589492{col 69}{space 3} .0063942
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4461157{col 28}{space 2} .1559373{col 39}{space 1}    2.86{col 48}{space 3}0.004{col 56}{space 4} .1404841{col 69}{space 3} .7517472
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .058688{col 28}{space 2} .0464818{col 39}{space 1}    1.26{col 48}{space 3}0.207{col 56}{space 4}-.0324147{col 69}{space 3} .1497907
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.0521859{col 28}{space 2} .1417462{col 39}{space 1}   -0.37{col 48}{space 3}0.713{col 56}{space 4}-.3300034{col 69}{space 3} .2256316
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6713646{col 28}{space 2} .1313187{col 39}{space 1}   -5.11{col 48}{space 3}0.000{col 56}{space 4}-.9287445{col 69}{space 3}-.4139847
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0077646{col 28}{space 2} .0025937{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4}  .002681{col 69}{space 3} .0128482
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0031843{col 28}{space 2} .3265344{col 39}{space 1}   -0.01{col 48}{space 3}0.992{col 56}{space 4}-.6431799{col 69}{space 3} .6368114
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-736.01598}  
Iteration 2:{space 3}log likelihood = {res:-735.51188}  
Iteration 3:{space 3}log likelihood = {res:-735.51162}  
Iteration 4:{space 3}log likelihood = {res:-735.51162}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}     67.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-735.51162{txt}{col 49}Pseudo R2{col 67}= {res}    0.0439

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}   .12895{col 26}{space 2} .2099223{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4}-.2824901{col 67}{space 3} .5403901
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1269743{col 26}{space 2} .1982669{col 37}{space 1}   -0.64{col 46}{space 3}0.522{col 54}{space 4}-.5155703{col 67}{space 3} .2616217
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.0715482{col 26}{space 2} .2045921{col 37}{space 1}   -0.35{col 46}{space 3}0.727{col 54}{space 4}-.4725414{col 67}{space 3} .3294449
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.1595903{col 26}{space 2} .1993376{col 37}{space 1}   -0.80{col 46}{space 3}0.423{col 54}{space 4}-.5502848{col 67}{space 3} .2311042
{txt}health_frame {c |}{col 14}{res}{space 2}  .580744{col 26}{space 2} .1314212{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .3231631{col 67}{space 3} .8383248
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0554683{col 26}{space 2} .1573225{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.2528781{col 67}{space 3} .3638147
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2}  .028449{col 26}{space 2} .1395286{col 37}{space 1}    0.20{col 46}{space 3}0.838{col 54}{space 4}-.2450221{col 67}{space 3} .3019201
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4498605{col 26}{space 2} .1553009{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .1454764{col 67}{space 3} .7542447
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0605901{col 26}{space 2} .0463431{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0302408{col 67}{space 3} .1514209
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0513903{col 26}{space 2} .1411402{col 37}{space 1}   -0.36{col 46}{space 3}0.716{col 54}{space 4}  -.32802{col 67}{space 3} .2252393
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6639694{col 26}{space 2} .1307816{col 37}{space 1}   -5.08{col 46}{space 3}0.000{col 54}{space 4}-.9202967{col 67}{space 3}-.4076421
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0078306{col 26}{space 2} .0025842{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0027656{col 67}{space 3} .0128956
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0721551{col 26}{space 2} .3125336{col 37}{space 1}    0.23{col 46}{space 3}0.817{col 54}{space 4}-.5403995{col 67}{space 3} .6847096
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: gender{c )-})  mcmcsize(200000) burnin(20000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss white education gop gender ideology_rs
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 78}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 27}{space 9}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 8}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 7}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 6}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 2}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 7}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 7}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 9}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 5}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 11}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 8}{res}{c -(}no_shop:gender{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 3}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 27}{space 9}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 78}
{p 0 4 0 78}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 50}MCMC iterations{col 67}={col 69}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 50}Burn-in{col 67}={col 69}{res}    20,000
{col 50}{txt}MCMC sample size{col 67}={col 69}{res}   200,000
{txt}{col 50}Number of obs{col 67}={col 69}{res}     1,346
{txt}{col 50}Acceptance rate{col 67}={col 69}{res}     .4389
{txt}{col 50}Efficiency:{col 63}min ={col 69}{res}   .007085
{col 63}{txt}avg ={col 69}{res}    .05363
{txt}Log marginal-likelihood = {res}-793.52148{col 63}{txt}max ={col 69}{res}     .1162
 
{txt}{hline 13}{col 14}{c TT}{hline 64}
{col 14}{c |}{col 63}Equal-tailed
{col 6}no_shop{col 14}{c |}{col 21}Mean{col 28}Std. Dev.{col 42}MCSE{col 51}Median{col 59}[95% Cred. Interval]
{res}{txt}{hline 13}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 7}cdc_m {c |}{col 14}{res}{space 1} .1289589{col 25}{space 2} .2127249{col 36}{space 2} .002198{col 46}{space 2} .1289898{col 57}{space 2}-.2886419{col 68}{space 2} .5442197
{txt}{space 6}pres_m {c |}{col 14}{res}{space 1}-.1323246{col 25}{space 2} .2001146{col 36}{space 2} .002207{col 46}{space 2}-.1329213{col 57}{space 2}-.5276888{col 68}{space 2} .2578165
{txt}{space 5}state_m {c |}{col 14}{res}{space 1}-.0747217{col 25}{space 2} .2073676{col 36}{space 2} .002266{col 46}{space 2}-.0736141{col 57}{space 2}-.4813583{col 68}{space 2} .3341088
{txt}{space 4}expert_m {c |}{col 14}{res}{space 1}-.1635965{col 25}{space 2} .2018573{col 36}{space 2} .002111{col 46}{space 2}-.1638038{col 57}{space 2}-.5588907{col 68}{space 2} .2329021
{txt}health_frame {c |}{col 14}{res}{space 1} .5888258{col 25}{space 2} .1322275{col 36}{space 2} .000911{col 46}{space 2}  .589032{col 57}{space 2} .3315047{col 68}{space 2} .8478876
{txt}{space 5}shelter {c |}{col 14}{res}{space 1} .0529704{col 25}{space 2} .1591841{col 36}{space 2} .002105{col 46}{space 2} .0542109{col 57}{space 2}-.2619513{col 68}{space 2} .3615473
{txt}{space 5}jobloss {c |}{col 14}{res}{space 1}  .031108{col 25}{space 2} .1401159{col 36}{space 2} .000959{col 46}{space 2} .0302359{col 57}{space 2}-.2418458{col 68}{space 2} .3069267
{txt}{space 7}white {c |}{col 14}{res}{space 1} .4530276{col 25}{space 2} .1557207{col 36}{space 2} .002005{col 46}{space 2} .4542273{col 57}{space 2} .1450976{col 68}{space 2}  .756593
{txt}{space 3}education {c |}{col 14}{res}{space 1}  .061698{col 25}{space 2} .0466208{col 36}{space 2} .000621{col 46}{space 2} .0616148{col 57}{space 2}-.0296262{col 68}{space 2} .1534623
{txt}{space 9}gop {c |}{col 14}{res}{space 1}-.0514224{col 25}{space 2} .1422011{col 36}{space 2} .000933{col 46}{space 2}-.0518688{col 57}{space 2} -.329953{col 68}{space 2} .2280476
{txt}{space 6}gender {c |}{col 14}{res}{space 1}-.6715076{col 25}{space 2}  .131093{col 36}{space 2} .001002{col 46}{space 2}-.6708366{col 57}{space 2}-.9303568{col 68}{space 2}-.4149636
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 1} .0078994{col 25}{space 2} .0025997{col 36}{space 2}  .00005{col 46}{space 2} .0078872{col 57}{space 2} .0028686{col 68}{space 2} .0130057
{txt}{space 7}_cons {c |}{col 14}{res}{space 1} .0779635{col 25}{space 2} .3085393{col 36}{space 2} .008196{col 46}{space 2} .0808842{col 57}{space 2}-.5268466{col 68}{space 2} .6918408
{txt}{hline 13}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 78}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. erase simdata2
{err}{p 0 4 2}
file simdata2
not found
{p_end}
{txt}{search r(601), local:r(601);}

{com}. erase simdata2.dta

. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: pres_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: gender{c )-})  mcmcsize(200000) burnin(20000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gender gop ideology_rs
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:gender{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    20,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   200,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,346
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4406
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .006897
{col 65}{txt}avg ={col 71}{res}    .03262
{txt}Log marginal-likelihood = {res}-802.99733{col 65}{txt}max ={col 71}{res}      .115
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1} .2177693{col 27}{space 2} .2698607{col 38}{space 2} .004162{col 48}{space 2}    .2182{col 59}{space 2}-.3105406{col 70}{space 2} .7476192
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1} -.190591{col 27}{space 2} .2524459{col 38}{space 2} .003903{col 48}{space 2}-.1884326{col 59}{space 2}-.6887832{col 70}{space 2} .3006203
{txt}{space 7}state_m {c |}{col 16}{res}{space 1} .0781498{col 27}{space 2} .2642549{col 38}{space 2} .004389{col 48}{space 2} .0783605{col 59}{space 2}-.4422551{col 70}{space 2} .5954408
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1} .1691426{col 27}{space 2} .2630195{col 38}{space 2} .004208{col 48}{space 2}  .170274{col 59}{space 2}-.3506054{col 70}{space 2} .6865213
{txt}{space 2}health_frame {c |}{col 16}{res}{space 1} .8503538{col 27}{space 2} .3060754{col 38}{space 2} .006431{col 48}{space 2} .8478581{col 59}{space 2} .2523244{col 70}{space 2} 1.457071
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0411756{col 27}{space 2} .1585991{col 38}{space 2} .001907{col 48}{space 2} .0426335{col 59}{space 2}-.2706637{col 70}{space 2} .3467045
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0361315{col 27}{space 2} .1410635{col 38}{space 2} .001055{col 48}{space 2}  .035307{col 59}{space 2}-.2382963{col 70}{space 2} .3136588
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 1}-.2308554{col 27}{space 2} .4404385{col 38}{space 2} .007532{col 48}{space 2} -.232076{col 59}{space 2}-1.092403{col 70}{space 2} .6386644
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 1} .1609938{col 27}{space 2} .4243082{col 38}{space 2}   .0071{col 48}{space 2} .1582793{col 59}{space 2}-.6732533{col 70}{space 2}  .998145
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 1}-.3919695{col 27}{space 2} .4321869{col 38}{space 2} .007771{col 48}{space 2} -.390025{col 59}{space 2}-1.246562{col 70}{space 2} .4508392
{txt}expert_frame_h {c |}{col 16}{res}{space 1}-.7959226{col 27}{space 2} .4134117{col 38}{space 2} .007275{col 48}{space 2}-.7975664{col 59}{space 2}-1.602992{col 70}{space 2}  .015589
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4485339{col 27}{space 2} .1574798{col 38}{space 2} .001919{col 48}{space 2} .4491481{col 59}{space 2} .1349866{col 70}{space 2} .7543068
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0597579{col 27}{space 2} .0468599{col 38}{space 2} .000624{col 48}{space 2} .0593801{col 59}{space 2} -.032272{col 70}{space 2} .1519904
{txt}{space 8}gender {c |}{col 16}{res}{space 1}-.6801651{col 27}{space 2} .1323991{col 38}{space 2} .001073{col 48}{space 2} -.679508{col 59}{space 2}-.9403152{col 70}{space 2}-.4220139
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.0499655{col 27}{space 2} .1426831{col 38}{space 2} .000941{col 48}{space 2}-.0499651{col 59}{space 2}-.3286426{col 70}{space 2} .2303972
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1} .0078242{col 27}{space 2} .0026451{col 38}{space 2} .000052{col 48}{space 2} .0078431{col 59}{space 2} .0025862{col 70}{space 2} .0129516
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .0078247{col 27}{space 2} .3310414{col 38}{space 2} .008913{col 48}{space 2}  .007582{col 59}{space 2} -.635636{col 70}{space 2} .6519628
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}
{com}. bayestest interval {c -(}no_shop: health_frame{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99743{col 25}{space 2}   0.05063{col 37}{space 2}  .000511
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:cdc_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: cdc_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .97552{col 25}{space 2}   0.15453{col 37}{space 2} .0007391
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:pres_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: pres_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .999825{col 25}{space 2}   0.01323{col 37}{space 2} .0000439
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:state_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: state_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .93866{col 25}{space 2}   0.23995{col 37}{space 2} .0012629
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: health_frame{c )-}+{c -(}no_shop:expert_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:health_frame{c )-}+{c -(}no_shop: expert_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .57643{col 25}{space 2}   0.49413{col 37}{space 2} .0031344
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: ideology_rs{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:ideology_rs{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .998195{col 25}{space 2}   0.04245{col 37}{space 2} .0003407
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: gender{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:gender{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}        1{col 25}{space 2}   0.00000{col 37}{space 2}        0
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: white{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:white{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .99763{col 25}{space 2}   0.04863{col 37}{space 2} .0002962
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. logit distance cdc_m pres_m state_m expert_m health_frame shelter jobloss white education gop gender ideology_rs

{txt}outcome does not vary; remember:
                                  0 = negative outcome,
        all other nonmissing values = positive outcome
{search r(2000), local:r(2000);}

{com}. ologit distance cdc_m pres_m state_m expert_m health_frame shelter jobloss white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1288.2703}  
Iteration 2:{space 3}log likelihood = {res:-1288.0084}  
Iteration 3:{space 3}log likelihood = {res:-1288.0084}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}     70.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1288.0084{txt}{col 49}Pseudo R2{col 67}= {res}    0.0267

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    distance{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}-.0459949{col 26}{space 2} .1838307{col 37}{space 1}   -0.25{col 46}{space 3}0.802{col 54}{space 4}-.4062965{col 67}{space 3} .3143066
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1787125{col 26}{space 2} .1766377{col 37}{space 1}   -1.01{col 46}{space 3}0.312{col 54}{space 4} -.524916{col 67}{space 3}  .167491
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.1429289{col 26}{space 2} .1814791{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4}-.4986215{col 67}{space 3} .2127637
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0246523{col 26}{space 2} .1789695{col 37}{space 1}   -0.14{col 46}{space 3}0.890{col 54}{space 4}-.3754261{col 67}{space 3} .3261215
{txt}health_frame {c |}{col 14}{res}{space 2} .1599638{col 26}{space 2} .1143472{col 37}{space 1}    1.40{col 46}{space 3}0.162{col 54}{space 4}-.0641526{col 67}{space 3} .3840802
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0663186{col 26}{space 2} .1380158{col 37}{space 1}    0.48{col 46}{space 3}0.631{col 54}{space 4}-.2041873{col 67}{space 3} .3368246
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2} .2122559{col 26}{space 2} .1252055{col 37}{space 1}    1.70{col 46}{space 3}0.090{col 54}{space 4}-.0331424{col 67}{space 3} .4576541
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4335384{col 26}{space 2} .1386922{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .1617067{col 67}{space 3} .7053701
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .132959{col 26}{space 2} .0411193{col 37}{space 1}    3.23{col 46}{space 3}0.001{col 54}{space 4} .0523666{col 67}{space 3} .2135513
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0976085{col 26}{space 2}  .124044{col 37}{space 1}   -0.79{col 46}{space 3}0.431{col 54}{space 4}-.3407303{col 67}{space 3} .1455133
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.5649769{col 26}{space 2} .1153475{col 37}{space 1}   -4.90{col 46}{space 3}0.000{col 54}{space 4}-.7910538{col 67}{space 3}-.3388999
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0113899{col 26}{space 2} .0023266{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .0068299{col 67}{space 3} .0159499
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.743907{col 26}{space 2} .3384266{col 54}{space 4}-3.407211{col 67}{space 3}-2.080603
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.711371{col 26}{space 2} .2972505{col 54}{space 4}-2.293971{col 67}{space 3}-1.128771
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.8060575{col 26}{space 2} .2840324{col 54}{space 4}-1.362751{col 67}{space 3}-.2493643
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} .7744341{col 26}{space 2} .2820139{col 54}{space 4}  .221697{col 67}{space 3} 1.327171
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit distance cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1323.3404}  
Iteration 1:{space 3}log likelihood = {res:-1286.8535}  
Iteration 2:{space 3}log likelihood = {res:-1286.5665}  
Iteration 3:{space 3}log likelihood = {res:-1286.5665}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     73.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1286.5665{txt}{col 49}Pseudo R2{col 67}= {res}    0.0278

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      distance{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-.0202358{col 28}{space 2} .2465825{col 39}{space 1}   -0.08{col 48}{space 3}0.935{col 56}{space 4}-.5035286{col 69}{space 3}  .463057
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.3015883{col 28}{space 2} .2349567{col 39}{space 1}   -1.28{col 48}{space 3}0.199{col 56}{space 4}-.7620949{col 69}{space 3} .1589184
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.1787169{col 28}{space 2} .2397622{col 39}{space 1}   -0.75{col 48}{space 3}0.456{col 56}{space 4}-.6486422{col 69}{space 3} .2912083
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1213523{col 28}{space 2}   .24315{col 39}{space 1}    0.50{col 48}{space 3}0.618{col 56}{space 4} -.355213{col 69}{space 3} .5979176
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .1581564{col 28}{space 2} .2579595{col 39}{space 1}    0.61{col 48}{space 3}0.540{col 56}{space 4}-.3474349{col 69}{space 3} .6637477
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0621214{col 28}{space 2} .1383456{col 39}{space 1}    0.45{col 48}{space 3}0.653{col 56}{space 4}-.2090309{col 69}{space 3} .3332737
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .2149219{col 28}{space 2} .1254158{col 39}{space 1}    1.71{col 48}{space 3}0.087{col 56}{space 4}-.0308886{col 69}{space 3} .4607324
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.0551722{col 28}{space 2} .3689857{col 39}{space 1}   -0.15{col 48}{space 3}0.881{col 56}{space 4}-.7783709{col 69}{space 3} .6680264
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .2708278{col 28}{space 2} .3551436{col 39}{space 1}    0.76{col 48}{space 3}0.446{col 56}{space 4}-.4252409{col 69}{space 3} .9668964
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}  .082696{col 28}{space 2} .3653309{col 39}{space 1}    0.23{col 48}{space 3}0.821{col 56}{space 4}-.6333395{col 69}{space 3} .7987314
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.3057813{col 28}{space 2}  .358833{col 39}{space 1}   -0.85{col 48}{space 3}0.394{col 56}{space 4}-1.009081{col 69}{space 3} .3975185
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4357643{col 28}{space 2} .1390428{col 39}{space 1}    3.13{col 48}{space 3}0.002{col 56}{space 4} .1632454{col 69}{space 3} .7082832
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .131619{col 28}{space 2} .0411767{col 39}{space 1}    3.20{col 48}{space 3}0.001{col 56}{space 4}  .050914{col 69}{space 3} .2123239
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.1010213{col 28}{space 2} .1242951{col 39}{space 1}   -0.81{col 48}{space 3}0.416{col 56}{space 4}-.3446353{col 69}{space 3} .1425927
{txt}{space 8}gender {c |}{col 16}{res}{space 2}  -.56539{col 28}{space 2} .1157004{col 39}{space 1}   -4.89{col 48}{space 3}0.000{col 56}{space 4}-.7921587{col 69}{space 3}-.3386213
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0113328{col 28}{space 2} .0023296{col 39}{space 1}    4.86{col 48}{space 3}0.000{col 56}{space 4} .0067669{col 69}{space 3} .0158988
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-2.758063{col 28}{space 2} .3512844{col 56}{space 4}-3.446568{col 69}{space 3}-2.069558
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.724983{col 28}{space 2} .3117644{col 56}{space 4} -2.33603{col 69}{space 3}-1.113936
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2}-.8187787{col 28}{space 2} .2990015{col 56}{space 4}-1.404811{col 69}{space 3}-.2327465
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} .7643362{col 28}{space 2} .2970156{col 56}{space 4} .1821963{col 69}{space 3} 1.346476
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit trust no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1579.9071}  
Iteration 1:{space 3}log likelihood = {res: -1521.644}  
Iteration 2:{space 3}log likelihood = {res:-1521.2449}  
Iteration 3:{space 3}log likelihood = {res:-1521.2446}  
Iteration 4:{space 3}log likelihood = {res:-1521.2446}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}17{txt}){col 67}= {res}    117.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1521.2446{txt}{col 49}Pseudo R2{col 67}= {res}    0.0371

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         trust{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}no_shop {c |}{col 16}{res}{space 2} .4857468{col 28}{space 2} .1208988{col 39}{space 1}    4.02{col 48}{space 3}0.000{col 56}{space 4} .2487896{col 69}{space 3}  .722704
{txt}{space 9}cdc_m {c |}{col 16}{res}{space 2}-.2965577{col 28}{space 2} .2257812{col 39}{space 1}   -1.31{col 48}{space 3}0.189{col 56}{space 4}-.7390806{col 69}{space 3} .1459652
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.5387627{col 28}{space 2} .2224679{col 39}{space 1}   -2.42{col 48}{space 3}0.015{col 56}{space 4}-.9747917{col 69}{space 3}-.1027336
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.3760067{col 28}{space 2} .2224519{col 39}{space 1}   -1.69{col 48}{space 3}0.091{col 56}{space 4}-.8120044{col 69}{space 3}  .059991
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2}-.1336295{col 28}{space 2} .2229653{col 39}{space 1}   -0.60{col 48}{space 3}0.549{col 56}{space 4}-.5706335{col 69}{space 3} .3033746
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} -.750836{col 28}{space 2} .2291232{col 39}{space 1}   -3.28{col 48}{space 3}0.001{col 56}{space 4}-1.199909{col 69}{space 3}-.3017628
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .3174769{col 28}{space 2}   .12752{col 39}{space 1}    2.49{col 48}{space 3}0.013{col 56}{space 4} .0675424{col 69}{space 3} .5674115
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .1081014{col 28}{space 2} .1132657{col 39}{space 1}    0.95{col 48}{space 3}0.340{col 56}{space 4}-.1138954{col 69}{space 3} .3300982
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2} 1.277727{col 28}{space 2} .3328725{col 39}{space 1}    3.84{col 48}{space 3}0.000{col 56}{space 4} .6253093{col 69}{space 3} 1.930146
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} 1.051367{col 28}{space 2} .3227422{col 39}{space 1}    3.26{col 48}{space 3}0.001{col 56}{space 4} .4188039{col 69}{space 3}  1.68393
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}  .781567{col 28}{space 2} .3262695{col 39}{space 1}    2.40{col 48}{space 3}0.017{col 56}{space 4} .1420905{col 69}{space 3} 1.421044
{txt}expert_frame_h {c |}{col 16}{res}{space 2} .7618436{col 28}{space 2} .3243614{col 39}{space 1}    2.35{col 48}{space 3}0.019{col 56}{space 4}  .126107{col 69}{space 3}  1.39758
{txt}{space 9}white {c |}{col 16}{res}{space 2}-.0612233{col 28}{space 2} .1322629{col 39}{space 1}   -0.46{col 48}{space 3}0.643{col 56}{space 4}-.3204539{col 69}{space 3} .1980072
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1102793{col 28}{space 2} .0376558{col 39}{space 1}    2.93{col 48}{space 3}0.003{col 56}{space 4} .0364753{col 69}{space 3} .1840834
{txt}{space 11}gop {c |}{col 16}{res}{space 2} .2856464{col 28}{space 2} .1144073{col 39}{space 1}    2.50{col 48}{space 3}0.013{col 56}{space 4} .0614124{col 69}{space 3} .5098805
{txt}{space 8}gender {c |}{col 16}{res}{space 2} .2775079{col 28}{space 2} .1069389{col 39}{space 1}    2.60{col 48}{space 3}0.009{col 56}{space 4} .0679115{col 69}{space 3} .4871044
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0131223{col 28}{space 2} .0021646{col 39}{space 1}    6.06{col 48}{space 3}0.000{col 56}{space 4} .0088798{col 69}{space 3} .0173649
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}/cut1 {c |}{col 16}{res}{space 2}-2.516242{col 28}{space 2} .3525652{col 56}{space 4}-3.207257{col 69}{space 3}-1.825227
{txt}{space 9}/cut2 {c |}{col 16}{res}{space 2}-1.380252{col 28}{space 2} .3023537{col 56}{space 4}-1.972854{col 69}{space 3}-.7876494
{txt}{space 9}/cut3 {c |}{col 16}{res}{space 2} .2531888{col 28}{space 2}  .283735{col 56}{space 4}-.3029216{col 69}{space 3} .8092992
{txt}{space 9}/cut4 {c |}{col 16}{res}{space 2} 2.367847{col 28}{space 2} .2916022{col 56}{space 4} 1.796317{col 69}{space 3} 2.939377
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-736.01598}  
Iteration 2:{space 3}log likelihood = {res:-735.51188}  
Iteration 3:{space 3}log likelihood = {res:-735.51162}  
Iteration 4:{space 3}log likelihood = {res:-735.51162}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}12{txt}){col 67}= {res}     67.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-735.51162{txt}{col 49}Pseudo R2{col 67}= {res}    0.0439

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}   .12895{col 26}{space 2} .2099223{col 37}{space 1}    0.61{col 46}{space 3}0.539{col 54}{space 4}-.2824901{col 67}{space 3} .5403901
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.1269743{col 26}{space 2} .1982669{col 37}{space 1}   -0.64{col 46}{space 3}0.522{col 54}{space 4}-.5155703{col 67}{space 3} .2616217
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.0715482{col 26}{space 2} .2045921{col 37}{space 1}   -0.35{col 46}{space 3}0.727{col 54}{space 4}-.4725414{col 67}{space 3} .3294449
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.1595903{col 26}{space 2} .1993376{col 37}{space 1}   -0.80{col 46}{space 3}0.423{col 54}{space 4}-.5502848{col 67}{space 3} .2311042
{txt}health_frame {c |}{col 14}{res}{space 2}  .580744{col 26}{space 2} .1314212{col 37}{space 1}    4.42{col 46}{space 3}0.000{col 54}{space 4} .3231631{col 67}{space 3} .8383248
{txt}{space 5}shelter {c |}{col 14}{res}{space 2} .0554683{col 26}{space 2} .1573225{col 37}{space 1}    0.35{col 46}{space 3}0.724{col 54}{space 4}-.2528781{col 67}{space 3} .3638147
{txt}{space 5}jobloss {c |}{col 14}{res}{space 2}  .028449{col 26}{space 2} .1395286{col 37}{space 1}    0.20{col 46}{space 3}0.838{col 54}{space 4}-.2450221{col 67}{space 3} .3019201
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4498605{col 26}{space 2} .1553009{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .1454764{col 67}{space 3} .7542447
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0605901{col 26}{space 2} .0463431{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.0302408{col 67}{space 3} .1514209
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0513903{col 26}{space 2} .1411402{col 37}{space 1}   -0.36{col 46}{space 3}0.716{col 54}{space 4}  -.32802{col 67}{space 3} .2252393
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6639694{col 26}{space 2} .1307816{col 37}{space 1}   -5.08{col 46}{space 3}0.000{col 54}{space 4}-.9202967{col 67}{space 3}-.4076421
{txt}{space 1}ideology_rs {c |}{col 14}{res}{space 2} .0078306{col 26}{space 2} .0025842{col 37}{space 1}    3.03{col 46}{space 3}0.002{col 54}{space 4} .0027656{col 67}{space 3} .0128956
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0721551{col 26}{space 2} .3125336{col 37}{space 1}    0.23{col 46}{space 3}0.817{col 54}{space 4}-.5403995{col 67}{space 3} .6847096
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.82287}  
Iteration 2:{space 3}log likelihood = {res:-732.14831}  
Iteration 3:{space 3}log likelihood = {res:-732.14766}  
Iteration 4:{space 3}log likelihood = {res:-732.14766}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     74.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.14766{txt}{col 49}Pseudo R2{col 67}= {res}    0.0483

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .2182732{col 28}{space 2}  .267345{col 39}{space 1}    0.82{col 48}{space 3}0.414{col 56}{space 4}-.3057134{col 69}{space 3} .7422598
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1855399{col 28}{space 2} .2499183{col 39}{space 1}   -0.74{col 48}{space 3}0.458{col 56}{space 4}-.6753708{col 69}{space 3}  .304291
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .0808928{col 28}{space 2} .2600986{col 39}{space 1}    0.31{col 48}{space 3}0.756{col 56}{space 4}-.4288911{col 69}{space 3} .5906768
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1690829{col 28}{space 2} .2600843{col 39}{space 1}    0.65{col 48}{space 3}0.516{col 56}{space 4} -.340673{col 69}{space 3} .6788387
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8441709{col 28}{space 2} .2998053{col 39}{space 1}    2.82{col 48}{space 3}0.005{col 56}{space 4} .2565633{col 69}{space 3} 1.431778
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0461846{col 28}{space 2} .1582116{col 39}{space 1}    0.29{col 48}{space 3}0.770{col 56}{space 4}-.2639045{col 69}{space 3} .3562737
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .0332535{col 28}{space 2} .1400279{col 39}{space 1}    0.24{col 48}{space 3}0.812{col 56}{space 4}-.2411961{col 69}{space 3} .3077031
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2408493{col 28}{space 2} .4332957{col 39}{space 1}   -0.56{col 48}{space 3}0.578{col 56}{space 4}-1.090093{col 69}{space 3} .6083946
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1484537{col 28}{space 2} .4164738{col 39}{space 1}    0.36{col 48}{space 3}0.722{col 56}{space 4}  -.66782{col 69}{space 3} .9647275
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3990044{col 28}{space 2} .4216742{col 39}{space 1}   -0.95{col 48}{space 3}0.344{col 56}{space 4}-1.225471{col 69}{space 3}  .427462
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7915489{col 28}{space 2} .4071213{col 39}{space 1}   -1.94{col 48}{space 3}0.052{col 56}{space 4}-1.589492{col 69}{space 3} .0063942
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4461157{col 28}{space 2} .1559373{col 39}{space 1}    2.86{col 48}{space 3}0.004{col 56}{space 4} .1404841{col 69}{space 3} .7517472
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .058688{col 28}{space 2} .0464818{col 39}{space 1}    1.26{col 48}{space 3}0.207{col 56}{space 4}-.0324147{col 69}{space 3} .1497907
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.0521859{col 28}{space 2} .1417462{col 39}{space 1}   -0.37{col 48}{space 3}0.713{col 56}{space 4}-.3300034{col 69}{space 3} .2256316
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6713646{col 28}{space 2} .1313187{col 39}{space 1}   -5.11{col 48}{space 3}0.000{col 56}{space 4}-.9287445{col 69}{space 3}-.4139847
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0077646{col 28}{space 2} .0025937{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4}  .002681{col 69}{space 3} .0128482
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0031843{col 28}{space 2} .3265344{col 39}{space 1}   -0.01{col 48}{space 3}0.992{col 56}{space 4}-.6431799{col 69}{space 3} .6368114
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m econ_frame shelter jobloss cdc_frame_e pres_frame_e state_frame_e expert_frame_e white education gop gender ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.82287}  
Iteration 2:{space 3}log likelihood = {res:-732.14831}  
Iteration 3:{space 3}log likelihood = {res:-732.14766}  
Iteration 4:{space 3}log likelihood = {res:-732.14766}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     74.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.14766{txt}{col 49}Pseudo R2{col 67}= {res}    0.0483

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}-.0225761{col 28}{space 2} .3418097{col 39}{space 1}   -0.07{col 48}{space 3}0.947{col 56}{space 4}-.6925107{col 69}{space 3} .6473585
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.0370862{col 28}{space 2} .3340108{col 39}{space 1}   -0.11{col 48}{space 3}0.912{col 56}{space 4}-.6917354{col 69}{space 3}  .617563
{txt}{space 7}state_m {c |}{col 16}{res}{space 2}-.3181115{col 28}{space 2} .3327172{col 39}{space 1}   -0.96{col 48}{space 3}0.339{col 56}{space 4}-.9702252{col 69}{space 3} .3340021
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} -.622466{col 28}{space 2} .3139504{col 39}{space 1}   -1.98{col 48}{space 3}0.047{col 56}{space 4}-1.237797{col 69}{space 3}-.0071346
{txt}{space 4}econ_frame {c |}{col 16}{res}{space 2}-.8441709{col 28}{space 2} .2998053{col 39}{space 1}   -2.82{col 48}{space 3}0.005{col 56}{space 4}-1.431778{col 69}{space 3}-.2565633
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0461846{col 28}{space 2} .1582116{col 39}{space 1}    0.29{col 48}{space 3}0.770{col 56}{space 4}-.2639045{col 69}{space 3} .3562737
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .0332535{col 28}{space 2} .1400279{col 39}{space 1}    0.24{col 48}{space 3}0.812{col 56}{space 4}-.2411961{col 69}{space 3} .3077031
{txt}{space 3}cdc_frame_e {c |}{col 16}{res}{space 2} .2408493{col 28}{space 2} .4332957{col 39}{space 1}    0.56{col 48}{space 3}0.578{col 56}{space 4}-.6083946{col 69}{space 3} 1.090093
{txt}{space 2}pres_frame_e {c |}{col 16}{res}{space 2}-.1484537{col 28}{space 2} .4164738{col 39}{space 1}   -0.36{col 48}{space 3}0.722{col 56}{space 4}-.9647275{col 69}{space 3}   .66782
{txt}{space 1}state_frame_e {c |}{col 16}{res}{space 2} .3990044{col 28}{space 2} .4216742{col 39}{space 1}    0.95{col 48}{space 3}0.344{col 56}{space 4} -.427462{col 69}{space 3} 1.225471
{txt}expert_frame_e {c |}{col 16}{res}{space 2} .7915489{col 28}{space 2} .4071213{col 39}{space 1}    1.94{col 48}{space 3}0.052{col 56}{space 4}-.0063942{col 69}{space 3} 1.589492
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4461157{col 28}{space 2} .1559373{col 39}{space 1}    2.86{col 48}{space 3}0.004{col 56}{space 4} .1404841{col 69}{space 3} .7517472
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .058688{col 28}{space 2} .0464818{col 39}{space 1}    1.26{col 48}{space 3}0.207{col 56}{space 4}-.0324147{col 69}{space 3} .1497907
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.0521859{col 28}{space 2} .1417462{col 39}{space 1}   -0.37{col 48}{space 3}0.713{col 56}{space 4}-.3300034{col 69}{space 3} .2256316
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6713646{col 28}{space 2} .1313187{col 39}{space 1}   -5.11{col 48}{space 3}0.000{col 56}{space 4}-.9287445{col 69}{space 3}-.4139847
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0077646{col 28}{space 2} .0025937{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4}  .002681{col 69}{space 3} .0128482
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .8409866{col 28}{space 2}  .362574{col 39}{space 1}    2.32{col 48}{space 3}0.020{col 56}{space 4} .1303547{col 69}{space 3} 1.551619
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. sum econ_frame

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}econ_frame {c |}{res}      1,346    .5341753    .4990161          0          1

{com}. tab health_frame no_shop

{txt}health_fra {c |}        no_shop
        me {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       222        497 {txt}{c |}{res}       719 
{txt}         1 {c |}{res}       126        501 {txt}{c |}{res}       627 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       348        998 {txt}{c |}{res}     1,346 

{com}. tabulate health_frame no_shop, chi2

{txt}health_fra {c |}        no_shop
        me {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       222        497 {txt}{c |}{res}       719 
{txt}         1 {c |}{res}       126        501 {txt}{c |}{res}       627 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       348        998 {txt}{c |}{res}     1,346 

{txt}          Pearson chi2({res}1{txt}) = {res} 20.3054  {txt} Pr = {res}0.000

{com}. tabulate health_frame no_shop, chi2 exact

{txt}health_fra {c |}        no_shop
        me {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       222        497 {txt}{c |}{res}       719 
{txt}         1 {c |}{res}       126        501 {txt}{c |}{res}       627 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       348        998 {txt}{c |}{res}     1,346 

{txt}          Pearson chi2({res}1{txt}) = {res} 20.3054  {txt} Pr = {res}0.000
{txt}           Fisher's exact =                 {res}0.000
{txt}   1-sided Fisher's exact =                 {res}0.000

{com}. ttest no_shop, by(health_frame)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    719{col 22} .6912378{col 34}  .017241{col 46} .4623044{col 58} .6573889{col 70} .7250867
       {txt}1 {c |}{res}{col 12}    627{col 22} .7990431{col 34} .0160158{col 46} .4010359{col 58} .7675918{col 70} .8304943
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  1,346{col 22} .7414562{col 34} .0119385{col 46} .4379971{col 58} .7180361{col 70} .7648762
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.1078052{col 34} .0237605{col 58} -.154417{col 70}-.0611935
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -4.5372
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1344

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000

{com}. erase simdata2.dta

. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: pres_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: econ_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_e{c )-}) block({c -(}no_shop: pres_frame_e{c )-}) block({c -(}no_shop: state_frame_e{c )-}) block({c -(}no_shop: expert_frame_e{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: gender{c )-})  mcmcsize(200000) burnin(20000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m pres_m state_m expert_m econ_frame shelter jobloss cdc_frame_e pres_frame_e state_frame_e expert_frame_e white education gender gop ideology_rs
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 80}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 29}{space 11}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:pres_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 8}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 6}{res}{c -(}no_shop:econ_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 9}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:cdc_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 4}{res}{c -(}no_shop:pres_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 3}{res}{c -(}no_shop:state_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 2}{res}{c -(}no_shop:expert_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 7}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 10}{res}{c -(}no_shop:gender{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 13}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 5}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 29}{space 11}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 80}
{p 0 4 0 80}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 52}MCMC iterations{col 69}={col 71}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 52}Burn-in{col 69}={col 71}{res}    20,000
{col 52}{txt}MCMC sample size{col 69}={col 71}{res}   200,000
{txt}{col 52}Number of obs{col 69}={col 71}{res}     1,346
{txt}{col 52}Acceptance rate{col 69}={col 71}{res}     .4381
{txt}{col 52}Efficiency:{col 65}min ={col 71}{res}   .005244
{col 65}{txt}avg ={col 71}{res}    .02887
{txt}Log marginal-likelihood = {res}-802.86151{col 65}{txt}max ={col 71}{res}     .1168
 
{txt}{hline 15}{col 16}{c TT}{hline 64}
{col 16}{c |}{col 65}Equal-tailed
{col 8}no_shop{col 16}{c |}{col 23}Mean{col 30}Std. Dev.{col 44}MCSE{col 53}Median{col 61}[95% Cred. Interval]
{res}{txt}{hline 15}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 9}cdc_m {c |}{col 16}{res}{space 1}-.0219007{col 27}{space 2} .3485749{col 38}{space 2} .008203{col 48}{space 2}-.0218308{col 59}{space 2}-.7041528{col 70}{space 2} .6586619
{txt}{space 8}pres_m {c |}{col 16}{res}{space 1}-.0397853{col 27}{space 2} .3384904{col 38}{space 2}  .00854{col 48}{space 2}-.0418762{col 59}{space 2}-.7050553{col 70}{space 2} .6308653
{txt}{space 7}state_m {c |}{col 16}{res}{space 1}-.3250656{col 27}{space 2} .3402787{col 38}{space 2} .008433{col 48}{space 2}-.3257466{col 59}{space 2}-.9890157{col 70}{space 2} .3483828
{txt}{space 6}expert_m {c |}{col 16}{res}{space 1}-.6350644{col 27}{space 2} .3193228{col 38}{space 2} .007917{col 48}{space 2}-.6360825{col 59}{space 2}-1.261342{col 70}{space 2}-.0067105
{txt}{space 4}econ_frame {c |}{col 16}{res}{space 1}-.8645252{col 27}{space 2} .3062582{col 38}{space 2}  .00923{col 48}{space 2}-.8651289{col 59}{space 2}-1.469119{col 70}{space 2}-.2670161
{txt}{space 7}shelter {c |}{col 16}{res}{space 1} .0415475{col 27}{space 2} .1585823{col 38}{space 2}  .00192{col 48}{space 2}  .042254{col 59}{space 2}-.2733224{col 70}{space 2} .3485848
{txt}{space 7}jobloss {c |}{col 16}{res}{space 1} .0356171{col 27}{space 2} .1412015{col 38}{space 2} .000942{col 48}{space 2} .0349634{col 59}{space 2}-.2400576{col 70}{space 2} .3149856
{txt}{space 3}cdc_frame_e {c |}{col 16}{res}{space 1} .2468219{col 27}{space 2} .4421721{col 38}{space 2} .010419{col 48}{space 2} .2436773{col 59}{space 2}-.6108993{col 70}{space 2} 1.120053
{txt}{space 2}pres_frame_e {c |}{col 16}{res}{space 1}-.1442235{col 27}{space 2} .4243313{col 38}{space 2} .010855{col 48}{space 2}-.1431264{col 59}{space 2}-.9806958{col 70}{space 2} .6812641
{txt}{space 1}state_frame_e {c |}{col 16}{res}{space 1} .4100383{col 27}{space 2} .4318333{col 38}{space 2} .010746{col 48}{space 2} .4110111{col 59}{space 2}-.4409955{col 70}{space 2} 1.251582
{txt}expert_frame_e {c |}{col 16}{res}{space 1} .8096416{col 27}{space 2} .4128175{col 38}{space 2} .010242{col 48}{space 2} .8101623{col 59}{space 2} .0016109{col 70}{space 2} 1.617392
{txt}{space 9}white {c |}{col 16}{res}{space 1} .4486329{col 27}{space 2} .1582684{col 38}{space 2} .002058{col 48}{space 2} .4491576{col 59}{space 2}  .135535{col 70}{space 2} .7592389
{txt}{space 5}education {c |}{col 16}{res}{space 1} .0583334{col 27}{space 2} .0471394{col 38}{space 2} .000598{col 48}{space 2} .0584592{col 59}{space 2} -.033902{col 70}{space 2} .1502077
{txt}{space 8}gender {c |}{col 16}{res}{space 1}-.6795762{col 27}{space 2} .1326482{col 38}{space 2} .001078{col 48}{space 2}-.6793391{col 59}{space 2}-.9398596{col 70}{space 2}-.4186378
{txt}{space 11}gop {c |}{col 16}{res}{space 1}-.0499497{col 27}{space 2} .1429385{col 38}{space 2} .000935{col 48}{space 2}-.0505408{col 59}{space 2}-.3294788{col 70}{space 2} .2301729
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 1}   .00779{col 27}{space 2} .0026441{col 38}{space 2} .000049{col 48}{space 2} .0078063{col 59}{space 2} .0025257{col 70}{space 2} .0129441
{txt}{space 9}_cons {c |}{col 16}{res}{space 1} .8730363{col 27}{space 2} .3647564{col 38}{space 2} .011264{col 48}{space 2} .8742382{col 59}{space 2} .1669894{col 70}{space 2} 1.590677
{txt}{hline 15}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}
{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .997775{col 25}{space 2}   0.04712{col 37}{space 2} .0004743
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:cdc_frame_e{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:cd c_frame_e{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .02505{col 25}{space 2}   0.15628{col 37}{space 2}  .000866
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:cdc_frame_e{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:cd c_frame_e{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .97495{col 25}{space 2}   0.15628{col 37}{space 2}  .000866
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:pres_frame_e{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:pr es_frame_e{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .000205{col 25}{space 2}   0.01432{col 37}{space 2}  .000055
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:pres_frame_e{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:pr es_frame_e{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .999795{col 25}{space 2}   0.01432{col 37}{space 2}  .000055
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:state_frame_e{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:st ate_frame_e{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .06286{col 25}{space 2}   0.24271{col 37}{space 2} .0014635
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:state_frame_e{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:st ate_frame_e{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .93714{col 25}{space 2}   0.24271{col 37}{space 2} .0014635
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:expert_frame_e{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:ex pert_frame_e{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .42252{col 25}{space 2}   0.49396{col 37}{space 2}   .00336
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. bayestest interval {c -(}no_shop: econ_frame{c )-}+{c -(}no_shop:expert_frame_e{c )-}, upper(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:econ_frame{c )-}+{c -(}no_shop:ex pert_frame_e{c )-} < 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}   .57748{col 25}{space 2}   0.49396{col 37}{space 2}   .00336
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study14R.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study14R.dta saved

{com}. erase simdata2.dta

. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Apr 2020, 18:00:21
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}17 Apr 2020, 10:42:33

{com}. sum cdc_frame_e- expert_frame_h

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}cdc_frame_e {c |}{res}      1,346    .0995542     .299516          0          1
{txt}{space 1}cdc_frame_h {c |}{res}      1,346    .0921248    .2893096          0          1
{txt}pres_frame_e {c |}{res}      1,346    .1121842    .3157103          0          1
{txt}pres_frame_h {c |}{res}      1,346    .0995542     .299516          0          1
{txt}state_fram~e {c |}{res}      1,346    .1032689    .3044229          0          1
{txt}{hline 13}{c +}{hline 57}
state_fram~h {c |}{res}      1,346    .0884101    .2839959          0          1
{txt}expert_fra~e {c |}{res}      1,346    .1092125    .3120215          0          1
{txt}expert_fra~h {c |}{res}      1,346    .0950966    .2934573          0          1

{com}. gen control_frame_h = health_frame* control_m

. gen control_frame_e = econ_frame* control_m

. sum cdc_frame_e- expert_frame_h control_frame_h control_frame_e

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}cdc_frame_e {c |}{res}      1,346    .0995542     .299516          0          1
{txt}{space 1}cdc_frame_h {c |}{res}      1,346    .0921248    .2893096          0          1
{txt}pres_frame_e {c |}{res}      1,346    .1121842    .3157103          0          1
{txt}pres_frame_h {c |}{res}      1,346    .0995542     .299516          0          1
{txt}state_fram~e {c |}{res}      1,346    .1032689    .3044229          0          1
{txt}{hline 13}{c +}{hline 57}
state_fram~h {c |}{res}      1,346    .0884101    .2839959          0          1
{txt}expert_fra~e {c |}{res}      1,346    .1092125    .3120215          0          1
{txt}expert_fra~h {c |}{res}      1,346    .0950966    .2934573          0          1
{txt}control_fr~h {c |}{res}      1,346    .0906389    .2872017          0          1
{txt}control_fr~e {c |}{res}      1,346    .1099554    .3129505          0          1

{com}. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: control_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: health_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_h{c )-}) block({c -(}no_shop: control_frame_h{c )-}) block({c -(}no_shop: state_frame_h{c )-}) block({c -(}no_shop: expert_frame_h{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: gender{c )-})  mcmcsize(200000) burnin(20000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m control_m state_m expert_m health_frame shelter jobloss cdc_frame_h control_frame_h state_frame_h expert_frame_h white education gender gop ideology_rs
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 81}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 30}{space 12}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 8}{res}{c -(}no_shop:control_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 9}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 5}{res}{c -(}no_shop:health_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 6}{res}{c -(}no_shop:cdc_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 2}{res}{c -(}no_shop:control_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 4}{res}{c -(}no_shop:state_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 3}{res}{c -(}no_shop:expert_frame_h{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 12}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 8}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 11}{res}{c -(}no_shop:gender{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 14}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 6}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 12}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 81}
{p 0 4 0 81}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 53}MCMC iterations{col 70}={col 72}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 53}Burn-in{col 70}={col 72}{res}    20,000
{col 53}{txt}MCMC sample size{col 70}={col 72}{res}   200,000
{txt}{col 53}Number of obs{col 70}={col 72}{res}     1,346
{txt}{col 53}Acceptance rate{col 70}={col 72}{res}     .4395
{txt}{col 53}Efficiency:{col 66}min ={col 72}{res}   .007096
{col 66}{txt}avg ={col 72}{res}    .03413
{txt}Log marginal-likelihood = {res} -802.8169{col 66}{txt}max ={col 72}{res}     .1177
 
{txt}{hline 16}{col 17}{c TT}{hline 64}
{col 17}{c |}{col 66}Equal-tailed
{col 9}no_shop{col 17}{c |}{col 24}Mean{col 31}Std. Dev.{col 45}MCSE{col 54}Median{col 62}[95% Cred. Interval]
{res}{txt}{hline 16}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 10}cdc_m {c |}{col 17}{res}{space 1} .4116945{col 28}{space 2} .2679012{col 39}{space 2}  .00422{col 49}{space 2} .4106211{col 60}{space 2}-.1098167{col 71}{space 2} .9397947
{txt}{space 6}control_m {c |}{col 17}{res}{space 1} .1898622{col 28}{space 2} .2522683{col 39}{space 2} .004122{col 49}{space 2} .1886567{col 60}{space 2}-.2997075{col 71}{space 2} .6878356
{txt}{space 8}state_m {c |}{col 17}{res}{space 1} .2727438{col 28}{space 2} .2592602{col 39}{space 2}  .00434{col 49}{space 2} .2716916{col 60}{space 2}-.2314471{col 71}{space 2} .7840168
{txt}{space 7}expert_m {c |}{col 17}{res}{space 1} .3626292{col 28}{space 2} .2577544{col 39}{space 2}  .00404{col 49}{space 2} .3604146{col 60}{space 2} -.139056{col 71}{space 2} .8716158
{txt}{space 3}health_frame {c |}{col 17}{res}{space 1} 1.011267{col 28}{space 2} .2967856{col 39}{space 2} .006131{col 49}{space 2} 1.007126{col 60}{space 2} .4416086{col 71}{space 2} 1.604013
{txt}{space 8}shelter {c |}{col 17}{res}{space 1} .0427254{col 28}{space 2} .1582654{col 39}{space 2} .002053{col 49}{space 2} .0432628{col 60}{space 2}-.2680044{col 71}{space 2} .3505217
{txt}{space 8}jobloss {c |}{col 17}{res}{space 1} .0366003{col 28}{space 2} .1407905{col 39}{space 2} .000941{col 49}{space 2} .0357454{col 60}{space 2}-.2366981{col 71}{space 2} .3159605
{txt}{space 4}cdc_frame_h {c |}{col 17}{res}{space 1}-.3937507{col 28}{space 2} .4356354{col 39}{space 2} .007274{col 49}{space 2}-.3951139{col 60}{space 2}-1.247721{col 71}{space 2} .4655087
{txt}control_frame_h {c |}{col 17}{res}{space 1}-.1491207{col 28}{space 2} .4234424{col 39}{space 2} .006928{col 49}{space 2}-.1502472{col 60}{space 2}-.9772376{col 71}{space 2} .6788839
{txt}{space 2}state_frame_h {c |}{col 17}{res}{space 1}-.5560803{col 28}{space 2} .4247581{col 39}{space 2} .007322{col 49}{space 2}-.5534423{col 60}{space 2}-1.399378{col 71}{space 2} .2671584
{txt}{space 1}expert_frame_h {c |}{col 17}{res}{space 1}-.9576561{col 28}{space 2} .4057562{col 39}{space 2} .006978{col 49}{space 2}-.9558036{col 60}{space 2}-1.757111{col 71}{space 2}-.1738966
{txt}{space 10}white {c |}{col 17}{res}{space 1}    .4503{col 28}{space 2} .1570099{col 39}{space 2} .001934{col 49}{space 2} .4498179{col 60}{space 2} .1447207{col 71}{space 2} .7576327
{txt}{space 6}education {c |}{col 17}{res}{space 1} .0598473{col 28}{space 2} .0470272{col 39}{space 2} .000606{col 49}{space 2}  .059656{col 60}{space 2}-.0319318{col 71}{space 2} .1519078
{txt}{space 9}gender {c |}{col 17}{res}{space 1}-.6802718{col 28}{space 2} .1326046{col 39}{space 2} .001063{col 49}{space 2} -.679556{col 60}{space 2}-.9412897{col 71}{space 2}-.4206142
{txt}{space 12}gop {c |}{col 17}{res}{space 1}-.0506707{col 28}{space 2} .1428536{col 39}{space 2} .000931{col 49}{space 2}-.0508597{col 60}{space 2}-.3305507{col 71}{space 2} .2292344
{txt}{space 4}ideology_rs {c |}{col 17}{res}{space 1} .0078788{col 28}{space 2} .0026185{col 39}{space 2}  .00005{col 49}{space 2} .0078956{col 60}{space 2} .0027156{col 71}{space 2} .0129139
{txt}{space 10}_cons {c |}{col 17}{res}{space 1}-.1916193{col 28}{space 2} .3428115{col 39}{space 2}   .0091{col 49}{space 2}-.1907355{col 60}{space 2}-.8553711{col 71}{space 2} .4704682
{txt}{hline 16}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}
{com}. bayestest interval {c -(}no_shop: control_m{c )-}+{c -(}no_shop: control_frame_h{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:control_m{c )-}+{c -(}no_shop:con trol_frame_h{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}    .5469{col 25}{space 2}   0.49780{col 37}{space 2} .0047215
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2
{err}{p 0 4 2}
file simdata2
not found
{p_end}
{txt}{search r(601), local:r(601);}

{com}. erase simdata2.dta

. bayes, block({c -(}no_shop: cdc_m{c )-}) block({c -(}no_shop: control_m{c )-}) block({c -(}no_shop: state_m{c )-}) block({c -(}no_shop: expert_m{c )-}) block({c -(}no_shop: econ_frame{c )-}) block({c -(}no_shop: shelter{c )-}) block({c -(}no_shop: jobloss{c )-}) block({c -(}no_shop: cdc_frame_e{c )-}) block({c -(}no_shop: control_frame_e{c )-}) block({c -(}no_shop: state_frame_e{c )-}) block({c -(}no_shop: expert_frame_e{c )-}) block({c -(}no_shop: white{c )-}) block({c -(}no_shop: education{c )-}) block({c -(}no_shop: gop{c )-}) block({c -(}no_shop: ideology_rs{c )-}) block({c -(}no_shop: gender{c )-})  mcmcsize(200000) burnin(20000) saving(simdata2) rseed(32306) prior({c -(}no_shop:{c )-}, uniform(-10,10)): logit no_shop cdc_m control_m state_m expert_m econ_frame shelter jobloss cdc_frame_e control_frame_e state_frame_e expert_frame_e white education gender gop ideology_rs
{res}  
{txt}Burn-in ...
{txt}Simulation ...
{res}
{txt}file simdata2.dta saved
{res}
{txt}Model summary
{txt}{hline 81}
{txt}Likelihood: 
{p 0 12}{space 2}{res:no_shop} ~ logit(xb_no_shop){p_end}

Priors: 
{p 0 30}{space 12}{res}{c -(}no_shop:cdc_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 8}{res}{c -(}no_shop:control_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:state_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 9}{res}{c -(}no_shop:expert_m{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 7}{res}{c -(}no_shop:econ_frame{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:shelter{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 10}{res}{c -(}no_shop:jobloss{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 6}{res}{c -(}no_shop:cdc_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 2}{res}{c -(}no_shop:control_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 4}{res}{c -(}no_shop:state_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 3}{res}{c -(}no_shop:expert_frame_e{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 12}{res}{c -(}no_shop:white{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 8}{res}{c -(}no_shop:education{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 11}{res}{c -(}no_shop:gender{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 14}{res}{c -(}no_shop:gop{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 6}{res}{c -(}no_shop:ideology_rs{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{p 0 30}{space 12}{res}{c -(}no_shop:_cons{c )-}{txt} ~ uniform(-10,10){space 33}(1){p_end}
{txt}{hline 81}
{p 0 4 0 81}
(1) Parameters are elements of the linear form xb_no_shop.
{p_end}

{res}{txt}Bayesian logistic regression{col 53}MCMC iterations{col 70}={col 72}{res}   220,000
{txt}Random-walk Metropolis-Hastings sampling{col 53}Burn-in{col 70}={col 72}{res}    20,000
{col 53}{txt}MCMC sample size{col 70}={col 72}{res}   200,000
{txt}{col 53}Number of obs{col 70}={col 72}{res}     1,346
{txt}{col 53}Acceptance rate{col 70}={col 72}{res}     .4431
{txt}{col 53}Efficiency:{col 66}min ={col 72}{res}   .005175
{col 66}{txt}avg ={col 72}{res}    .02941
{txt}Log marginal-likelihood = {res}-802.81264{col 66}{txt}max ={col 72}{res}     .1202
 
{txt}{hline 16}{col 17}{c TT}{hline 64}
{col 17}{c |}{col 66}Equal-tailed
{col 9}no_shop{col 17}{c |}{col 24}Mean{col 31}Std. Dev.{col 45}MCSE{col 54}Median{col 62}[95% Cred. Interval]
{res}{txt}{hline 16}{c +}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{space 10}cdc_m {c |}{col 17}{res}{space 1} .0206437{col 28}{space 2} .3439376{col 39}{space 2} .008283{col 49}{space 2} .0199252{col 60}{space 2}-.6508504{col 71}{space 2}  .695099
{txt}{space 6}control_m {c |}{col 17}{res}{space 1} .0429307{col 28}{space 2} .3387536{col 39}{space 2} .008446{col 49}{space 2} .0434265{col 60}{space 2}-.6183403{col 71}{space 2} .7050535
{txt}{space 8}state_m {c |}{col 17}{res}{space 1}-.2818707{col 28}{space 2} .3335426{col 39}{space 2}  .00846{col 49}{space 2}-.2849268{col 60}{space 2}-.9325356{col 71}{space 2} .3708208
{txt}{space 7}expert_m {c |}{col 17}{res}{space 1}-.5921895{col 28}{space 2} .3150692{col 39}{space 2} .007873{col 49}{space 2}-.5901534{col 60}{space 2}-1.211457{col 71}{space 2} .0177684
{txt}{space 5}econ_frame {c |}{col 17}{res}{space 1}-1.005633{col 28}{space 2} .2980906{col 39}{space 2} .008824{col 49}{space 2}-1.004852{col 60}{space 2}-1.594181{col 71}{space 2}-.4297876
{txt}{space 8}shelter {c |}{col 17}{res}{space 1} .0413528{col 28}{space 2} .1593102{col 39}{space 2} .002013{col 49}{space 2} .0420824{col 60}{space 2}-.2723016{col 71}{space 2} .3514713
{txt}{space 8}jobloss {c |}{col 17}{res}{space 1} .0362912{col 28}{space 2} .1410824{col 39}{space 2}  .00091{col 49}{space 2} .0351641{col 60}{space 2}-.2375794{col 71}{space 2} .3153375
{txt}{space 4}cdc_frame_e {c |}{col 17}{res}{space 1} .3889488{col 28}{space 2} .4362807{col 39}{space 2} .010208{col 49}{space 2} .3868223{col 60}{space 2}-.4710953{col 71}{space 2} 1.240638
{txt}control_frame_e {c |}{col 17}{res}{space 1} .1436055{col 28}{space 2} .4253838{col 39}{space 2} .010338{col 49}{space 2} .1457396{col 60}{space 2}-.6949911{col 71}{space 2} .9791625
{txt}{space 2}state_frame_e {c |}{col 17}{res}{space 1} .5511792{col 28}{space 2} .4279105{col 39}{space 2} .010541{col 49}{space 2} .5520682{col 60}{space 2}-.2908118{col 71}{space 2} 1.382969
{txt}{space 1}expert_frame_e {c |}{col 17}{res}{space 1} .9502504{col 28}{space 2} .4085907{col 39}{space 2} .009835{col 49}{space 2} .9501183{col 60}{space 2} .1454729{col 71}{space 2} 1.747436
{txt}{space 10}white {c |}{col 17}{res}{space 1} .4468141{col 28}{space 2} .1578215{col 39}{space 2} .002024{col 49}{space 2} .4480048{col 60}{space 2} .1351363{col 71}{space 2} .7539852
{txt}{space 6}education {c |}{col 17}{res}{space 1} .0593999{col 28}{space 2} .0467477{col 39}{space 2} .000615{col 49}{space 2} .0590734{col 60}{space 2}-.0319407{col 71}{space 2} .1523714
{txt}{space 9}gender {c |}{col 17}{res}{space 1} -.680775{col 28}{space 2} .1329179{col 39}{space 2} .001073{col 49}{space 2}-.6803085{col 60}{space 2} -.942895{col 71}{space 2}-.4218663
{txt}{space 12}gop {c |}{col 17}{res}{space 1}-.0496384{col 28}{space 2} .1426217{col 39}{space 2}  .00092{col 49}{space 2}-.0499785{col 60}{space 2}-.3272389{col 71}{space 2} .2309187
{txt}{space 4}ideology_rs {c |}{col 17}{res}{space 1} .0077831{col 28}{space 2} .0026615{col 39}{space 2}  .00005{col 49}{space 2} .0077994{col 60}{space 2} .0025225{col 71}{space 2} .0129504
{txt}{space 10}_cons {c |}{col 17}{res}{space 1} .8291397{col 28}{space 2} .3783988{col 39}{space 2} .011762{col 49}{space 2} .8249671{col 60}{space 2} .1009526{col 71}{space 2} 1.573333
{txt}{hline 16}{c BT}{hline 10}{hline 11}{hline 10}{hline 11}{hline 11}{hline 11}
{res}{txt}{p 0 6 0 81}Note: There is a {help j_bayesmh_highcorr:high autocorrelation} after 500 lags.{p_end}
{res}
{com}. bayestest interval {c -(}no_shop: control_m{c )-}+{c -(}no_shop: control_frame_e{c )-}, lower(0)
{res}
{txt}Interval tests{col 20}MCMC sample size{col 37}={col 38}{res}   200,000

{txt}{p 0 15 0 47}{space 7}prob1 : {res}{c -(}no_shop:control_m{c )-}+{c -(}no_shop:con trol_frame_e{c )-} > 0{txt}{p_end}
 
{hline 13}{col 14}{c TT}{hline 33}
{col 14}{c |}{col 21}Mean{col 29}Std. Dev.{col 44}MCSE
{res}{txt}{hline 13}{c +}{hline 10}{hline 12}{hline 11}
{space 7}prob1 {c |}{col 14}{res}{space 1}  .768985{col 25}{space 2}   0.42148{col 37}{space 2} .0034429
{txt}{hline 13}{c BT}{hline 10}{hline 12}{hline 11}

{com}. erase simdata2.dta

. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}17 Apr 2020, 12:25:30
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Apr 2020, 18:23:13

{com}. sum no_shop

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}no_shop {c |}{res}      1,346    .7414562    .4379971          0          1

{com}. power twomeans .74 (.78 .80 .82 .84), power(0.8 0.9) sd(.44) graph
{res}
{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-754.42967}  
Iteration 2:{space 3}log likelihood = {res:-754.25579}  
Iteration 3:{space 3}log likelihood = {res:-754.25574}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}     30.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0004
{txt}Log likelihood = {res}-754.25574{txt}{col 49}Pseudo R2{col 67}= {res}    0.0195

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2}  .298149{col 28}{space 2} .2617079{col 39}{space 1}    1.14{col 48}{space 3}0.255{col 56}{space 4}-.2147891{col 69}{space 3} .8110871
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1175726{col 28}{space 2} .2435918{col 39}{space 1}   -0.48{col 48}{space 3}0.629{col 56}{space 4}-.5950038{col 69}{space 3} .3598585
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .1337418{col 28}{space 2} .2542203{col 39}{space 1}    0.53{col 48}{space 3}0.599{col 56}{space 4}-.3645209{col 69}{space 3} .6320045
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .2465675{col 28}{space 2} .2536403{col 39}{space 1}    0.97{col 48}{space 3}0.331{col 56}{space 4}-.2505583{col 69}{space 3} .7436932
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8108279{col 28}{space 2} .2931957{col 39}{space 1}    2.77{col 48}{space 3}0.006{col 56}{space 4}  .236175{col 69}{space 3} 1.385481
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2220708{col 28}{space 2}  .425762{col 39}{space 1}   -0.52{col 48}{space 3}0.602{col 56}{space 4}-1.056549{col 69}{space 3} .6124075
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1774804{col 28}{space 2} .4089973{col 39}{space 1}    0.43{col 48}{space 3}0.664{col 56}{space 4}-.6241394{col 69}{space 3} .9791003
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3234504{col 28}{space 2} .4131837{col 39}{space 1}   -0.78{col 48}{space 3}0.434{col 56}{space 4}-1.133276{col 69}{space 3} .4863749
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7834435{col 28}{space 2} .3988885{col 39}{space 1}   -1.96{col 48}{space 3}0.050{col 56}{space 4}-1.565251{col 69}{space 3}-.0016365
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .7032995{col 28}{space 2} .1746688{col 39}{space 1}    4.03{col 48}{space 3}0.000{col 56}{space 4} .3609551{col 69}{space 3} 1.045644
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. findit ritest

. logit no_shop cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h, a(strata) robust
{err}option {bf:a()} not allowed
{txt}{search r(198), local:r(198);}

{com}. areg no_shop cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h, a(strata) robust
{err}variable {bf}strata{sf} not found
(error in option {bf:absorb()})
{txt}{search r(111), local:r(111);}

{com}. ssc install ritest
{txt}checking {hilite:ritest} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. ritest treatment (_b[treatment]/_se[treatment]): logit no_shop cdc_m pres_m state_m expert_m health_frame
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{err}[treatment] not found
error in expression: _b[treatment]/_se[treatment]
{txt}{search r(111), local:r(111);}

{com}. ritest cdc_m pres_m state_m expert_m health_frame (_b[cdc_m pres_m state_m expert_m health_frame]/_se[cdc_m pres_m state_m expert_m health_frame]): logit no_shop cdc_m pres_m state_m expert_m health_frame
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{err}cdc_mpres_mstate_mexpert_mhealth_frame invalid name
error in expression: _b[cdc_m pres_m state_m expert_m health_frame]/_se[cdc_m pres_m state_m expert_m health_frame]
{txt}{search r(198), local:r(198);}

{com}. ritest cdc_m (_b[cdc_m]/_se[cdc_m]): logit no_shop cdc_m pres_m state_m expert_m health_frame
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop cdc_m pres_m state_m expert_m health_frame{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[cdc_m]/_se[cdc_m]}{p_end}
  res. var(s):  cdc_m
   Resampling:  Permuting cdc_m
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} 1.053952{col 27}     31{col 35}    100{col 43} 0.3100{col 51} 0.0462{col 59} .2212888{col 69}{space 1} .4103146
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest cdc_m (_b[cdc_m]/_se[cdc_m]), cluster(health_frame): logit no_shop cdc_m pres_m state_m expert_m health_frame
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{err}cdc_m does not seem to be constant within clusters
{txt}{search r(9999), local:r(9999);}

{com}. ritest cdc_m (_b[cdc_m]/_se[cdc_m]), strata(health_frame): logit no_shop cdc_m pres_m state_m expert_m health_frame
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-757.80528}  
Iteration 2:{space 3}log likelihood = {res:-757.73887}  
Iteration 3:{space 3}log likelihood = {res:-757.73887}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     23.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res}-757.73887{txt}{col 49}Pseudo R2{col 67}= {res}    0.0150

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2}  .216599{col 26}{space 2} .2055113{col 37}{space 1}    1.05{col 46}{space 3}0.292{col 54}{space 4}-.1861957{col 67}{space 3} .6193936
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2}-.0479026{col 26}{space 2}  .193606{col 37}{space 1}   -0.25{col 46}{space 3}0.805{col 54}{space 4}-.4273635{col 67}{space 3} .3315582
{txt}{space 5}state_m {c |}{col 14}{res}{space 2} .0118767{col 26}{space 2} .1996761{col 37}{space 1}    0.06{col 46}{space 3}0.953{col 54}{space 4}-.3794812{col 67}{space 3} .4032346
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.0768621{col 26}{space 2} .1946259{col 37}{space 1}   -0.39{col 46}{space 3}0.693{col 54}{space 4}-.4583218{col 67}{space 3} .3045976
{txt}health_frame {c |}{col 14}{res}{space 2} .5733059{col 26}{space 2} .1283828{col 37}{space 1}    4.47{col 46}{space 3}0.000{col 54}{space 4} .3216803{col 67}{space 3} .8249315
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .791024{col 26}{space 2} .1477418{col 37}{space 1}    5.35{col 46}{space 3}0.000{col 54}{space 4} .5014553{col 67}{space 3} 1.080593
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop cdc_m pres_m state_m expert_m health_frame{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[cdc_m]/_se[cdc_m]}{p_end}
  res. var(s):  cdc_m
   Resampling:  Permuting cdc_m
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  health_frame
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2} 1.053952{col 27}     26{col 35}    100{col 43} 0.2600{col 51} 0.0439{col 59} .1773944{col 69}{space 1} .3573121
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(health_frame): logit treatment gender gop education white
{txt}(running {bf:logit} on estimation sample)

outcome does not vary; remember:
                                  0 = negative outcome,
        all other nonmissing values = positive outcome
{search r(2000), local:r(2000);}

{com}. ritest treatment (_b[treatment]/_se[treatment]): logit no_shop treatment gender gop education white
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-751.24306}  
Iteration 2:{space 3}log likelihood = {res:-751.09726}  
Iteration 3:{space 3}log likelihood = {res:-751.09725}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     36.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-751.09725{txt}{col 49}Pseudo R2{col 67}= {res}    0.0236

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0143755{col 26}{space 2} .0220935{col 37}{space 1}   -0.65{col 46}{space 3}0.515{col 54}{space 4} -.057678{col 67}{space 3} .0289271
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6524548{col 26}{space 2} .1288541{col 37}{space 1}   -5.06{col 46}{space 3}0.000{col 54}{space 4}-.9050042{col 67}{space 3}-.3999054
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.1096637{col 26}{space 2}  .138784{col 37}{space 1}   -0.79{col 46}{space 3}0.429{col 54}{space 4}-.3816753{col 67}{space 3} .1623479
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0613108{col 26}{space 2} .0455963{col 37}{space 1}    1.34{col 46}{space 3}0.179{col 54}{space 4}-.0280562{col 67}{space 3} .1506779
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4484591{col 26}{space 2} .1519742{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .1505952{col 67}{space 3}  .746323
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .9706072{col 26}{space 2} .2217296{col 37}{space 1}    4.38{col 46}{space 3}0.000{col 54}{space 4} .5360251{col 67}{space 3} 1.405189
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment gender gop education white{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000001
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  none
       {txt}Strata{res}:  1

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.6506634{col 27}     51{col 35}    100{col 43} 0.5100{col 51} 0.0500{col 59} .4080363{col 69}{space 1} .6113558
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), cluster(gender): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{err}treatment does not seem to be constant within clusters
{txt}{search r(9999), local:r(9999);}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gender): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gender
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     41{col 35}    100{col 43} 0.4100{col 51} 0.0492{col 59}   .31262{col 69}{space 1} .5128558
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(white): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  white
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     43{col 35}    100{col 43} 0.4300{col 51} 0.0495{col 59}  .331391{col 69}{space 1} .5328663
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gop): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gop
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     49{col 35}    100{col 43} 0.4900{col 51} 0.0500{col 59} .3886442{col 69}{space 1} .5919637
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gov_ideology): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gov_ideology
       {txt}Strata{res}:  10

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     35{col 35}    100{col 43} 0.3500{col 51} 0.0477{col 59} .2572938{col 69}{space 1} .4518494
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), cluster(health_frame) strata(gov_ideology): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{err}treatment does not seem to be constant within clusters
{txt}{search r(9999), local:r(9999);}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(income): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  income
       {txt}Strata{res}:  7

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     34{col 35}    100{col 43} 0.3400{col 51} 0.0474{col 59} .2482235{col 69}{space 1} .4415333
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. logit no_shop treatment gender income gop white education gov_ideology

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-746.23241}  
Iteration 2:{space 3}log likelihood = {res:-746.00272}  
Iteration 3:{space 3}log likelihood = {res:-746.00269}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}7{txt}){col 67}= {res}     46.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-746.00269{txt}{col 49}Pseudo R2{col 67}= {res}    0.0303

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0112237{col 26}{space 2} .0222387{col 37}{space 1}   -0.50{col 46}{space 3}0.614{col 54}{space 4}-.0548107{col 67}{space 3} .0323633
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6548875{col 26}{space 2} .1298734{col 37}{space 1}   -5.04{col 46}{space 3}0.000{col 54}{space 4}-.9094348{col 67}{space 3}-.4003403
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0383874{col 26}{space 2}  .032878{col 37}{space 1}    1.17{col 46}{space 3}0.243{col 54}{space 4}-.0260523{col 67}{space 3}  .102827
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.1096114{col 26}{space 2} .1398194{col 37}{space 1}   -0.78{col 46}{space 3}0.433{col 54}{space 4}-.3836523{col 67}{space 3} .1644296
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4855103{col 26}{space 2} .1535862{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .1844868{col 67}{space 3} .7865338
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .023968{col 26}{space 2} .0512964{col 37}{space 1}    0.47{col 46}{space 3}0.640{col 54}{space 4}-.0765711{col 67}{space 3} .1245071
{txt}gov_ideology {c |}{col 14}{res}{space 2} .0825742{col 26}{space 2} .0283048{col 37}{space 1}    2.92{col 46}{space 3}0.004{col 54}{space 4} .0270977{col 67}{space 3} .1380506
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1226688{col 26}{space 2} .3540247{col 37}{space 1}    0.35{col 46}{space 3}0.729{col 54}{space 4}-.5712069{col 67}{space 3} .8165445
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop treatment

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop t1 t2 t3 t4 t5 t6 t7 t8 c1 gender income gop white education gov_ideology

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-548.09652}  
Iteration 2:{space 3}log likelihood = {res:-537.57898}  
Iteration 3:{space 3}log likelihood = {res:-537.48382}  
Iteration 4:{space 3}log likelihood = {res:-537.48377}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    463.59
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-537.48377{txt}{col 49}Pseudo R2{col 67}= {res}    0.3013

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t1 {c |}{col 14}{res}{space 2} 2.073125{col 26}{space 2} .1899559{col 37}{space 1}   10.91{col 46}{space 3}0.000{col 54}{space 4} 1.700818{col 67}{space 3} 2.445432
{txt}{space 10}t2 {c |}{col 14}{res}{space 2} 2.325482{col 26}{space 2} .2173781{col 37}{space 1}   10.70{col 46}{space 3}0.000{col 54}{space 4} 1.899428{col 67}{space 3} 2.751535
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} 2.329145{col 26}{space 2} .2123571{col 37}{space 1}   10.97{col 46}{space 3}0.000{col 54}{space 4} 1.912932{col 67}{space 3} 2.745357
{txt}{space 10}t4 {c |}{col 14}{res}{space 2}  1.90061{col 26}{space 2} .1764203{col 37}{space 1}   10.77{col 46}{space 3}0.000{col 54}{space 4} 1.554833{col 67}{space 3} 2.246388
{txt}{space 10}t5 {c |}{col 14}{res}{space 2}  2.00642{col 26}{space 2}  .185274{col 37}{space 1}   10.83{col 46}{space 3}0.000{col 54}{space 4} 1.643289{col 67}{space 3}  2.36955
{txt}{space 10}t6 {c |}{col 14}{res}{space 2} 2.201656{col 26}{space 2} .2103514{col 37}{space 1}   10.47{col 46}{space 3}0.000{col 54}{space 4} 1.789375{col 67}{space 3} 2.613937
{txt}{space 10}t7 {c |}{col 14}{res}{space 2} 2.058259{col 26}{space 2} .1918161{col 37}{space 1}   10.73{col 46}{space 3}0.000{col 54}{space 4} 1.682307{col 67}{space 3} 2.434212
{txt}{space 10}t8 {c |}{col 14}{res}{space 2} 2.037356{col 26}{space 2} .1822744{col 37}{space 1}   11.18{col 46}{space 3}0.000{col 54}{space 4} 1.680105{col 67}{space 3} 2.394607
{txt}{space 10}c1 {c |}{col 14}{res}{space 2} 1.985173{col 26}{space 2} .1786592{col 37}{space 1}   11.11{col 46}{space 3}0.000{col 54}{space 4} 1.635008{col 67}{space 3} 2.335339
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.5090141{col 26}{space 2} .1594845{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4} -.821598{col 67}{space 3}-.1964303
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0411881{col 26}{space 2} .0399696{col 37}{space 1}    1.03{col 46}{space 3}0.303{col 54}{space 4}-.0371509{col 67}{space 3}  .119527
{txt}{space 9}gop {c |}{col 14}{res}{space 2} -.196132{col 26}{space 2} .1714912{col 37}{space 1}   -1.14{col 46}{space 3}0.253{col 54}{space 4}-.5322486{col 67}{space 3} .1399845
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4969355{col 26}{space 2} .1888785{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .1267405{col 67}{space 3} .8671305
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0204022{col 26}{space 2} .0629794{col 37}{space 1}    0.32{col 46}{space 3}0.746{col 54}{space 4}-.1030351{col 67}{space 3} .1438396
{txt}gov_ideology {c |}{col 14}{res}{space 2} .0618897{col 26}{space 2} .0349251{col 37}{space 1}    1.77{col 46}{space 3}0.076{col 54}{space 4}-.0065622{col 67}{space 3} .1303417
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.784966{col 26}{space 2} .4346858{col 37}{space 1}   -6.41{col 46}{space 3}0.000{col 54}{space 4}-3.636934{col 67}{space 3}-1.932997
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop t1 t2 t3 t4 t5 t6 t7 t8 c1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-556.70994}  
Iteration 2:{space 3}log likelihood = {res:-548.47474}  
Iteration 3:{space 3}log likelihood = {res:-548.41033}  
Iteration 4:{space 3}log likelihood = {res:-548.41031}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}9{txt}){col 67}= {res}    441.73
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-548.41031{txt}{col 49}Pseudo R2{col 67}= {res}    0.2871

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}t1 {c |}{col 14}{res}{space 2} 2.113833{col 26}{space 2} .1889638{col 37}{space 1}   11.19{col 46}{space 3}0.000{col 54}{space 4} 1.743471{col 67}{space 3} 2.484196
{txt}{space 10}t2 {c |}{col 14}{res}{space 2} 2.371561{col 26}{space 2} .2172617{col 37}{space 1}   10.92{col 46}{space 3}0.000{col 54}{space 4} 1.945736{col 67}{space 3} 2.797386
{txt}{space 10}t3 {c |}{col 14}{res}{space 2} 2.364491{col 26}{space 2} .2103622{col 37}{space 1}   11.24{col 46}{space 3}0.000{col 54}{space 4} 1.952188{col 67}{space 3} 2.776793
{txt}{space 10}t4 {c |}{col 14}{res}{space 2} 1.930229{col 26}{space 2} .1737262{col 37}{space 1}   11.11{col 46}{space 3}0.000{col 54}{space 4} 1.589732{col 67}{space 3} 2.270726
{txt}{space 10}t5 {c |}{col 14}{res}{space 2} 2.041493{col 26}{space 2} .1826878{col 37}{space 1}   11.17{col 46}{space 3}0.000{col 54}{space 4} 1.683431{col 67}{space 3} 2.399554
{txt}{space 10}t6 {c |}{col 14}{res}{space 2} 2.255344{col 26}{space 2} .2081584{col 37}{space 1}   10.83{col 46}{space 3}0.000{col 54}{space 4} 1.847361{col 67}{space 3} 2.663327
{txt}{space 10}t7 {c |}{col 14}{res}{space 2} 2.103203{col 26}{space 2} .1911126{col 37}{space 1}   11.01{col 46}{space 3}0.000{col 54}{space 4}  1.72863{col 67}{space 3} 2.477777
{txt}{space 10}t8 {c |}{col 14}{res}{space 2} 2.091167{col 26}{space 2} .1821858{col 37}{space 1}   11.48{col 46}{space 3}0.000{col 54}{space 4} 1.734089{col 67}{space 3} 2.448244
{txt}{space 10}c1 {c |}{col 14}{res}{space 2} 1.982404{col 26}{space 2} .1766296{col 37}{space 1}   11.22{col 46}{space 3}0.000{col 54}{space 4} 1.636217{col 67}{space 3} 2.328592
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.998238{col 26}{space 2} .1862975{col 37}{space 1}  -10.73{col 46}{space 3}0.000{col 54}{space 4}-2.363375{col 67}{space 3}-1.633102
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame gender income gop white education gov_ideology

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-735.35048}  
Iteration 2:{space 3}log likelihood = {res:-734.82655}  
Iteration 3:{space 3}log likelihood = {res:-734.82627}  
Iteration 4:{space 3}log likelihood = {res:-734.82627}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     68.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-734.82627{txt}{col 49}Pseudo R2{col 67}= {res}    0.0448

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}cdc_m {c |}{col 14}{res}{space 2} .1314847{col 26}{space 2} .2098858{col 37}{space 1}    0.63{col 46}{space 3}0.531{col 54}{space 4}-.2798839{col 67}{space 3} .5428534
{txt}{space 6}pres_m {c |}{col 14}{res}{space 2} -.119651{col 26}{space 2} .1980187{col 37}{space 1}   -0.60{col 46}{space 3}0.546{col 54}{space 4}-.5077604{col 67}{space 3} .2684585
{txt}{space 5}state_m {c |}{col 14}{res}{space 2}-.0787589{col 26}{space 2} .2044163{col 37}{space 1}   -0.39{col 46}{space 3}0.700{col 54}{space 4}-.4794075{col 67}{space 3} .3218897
{txt}{space 4}expert_m {c |}{col 14}{res}{space 2}-.1716464{col 26}{space 2} .1993166{col 37}{space 1}   -0.86{col 46}{space 3}0.389{col 54}{space 4}-.5622997{col 67}{space 3} .2190069
{txt}health_frame {c |}{col 14}{res}{space 2} .5793164{col 26}{space 2} .1313301{col 37}{space 1}    4.41{col 46}{space 3}0.000{col 54}{space 4} .3219141{col 67}{space 3} .8367188
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.6757683{col 26}{space 2} .1311897{col 37}{space 1}   -5.15{col 46}{space 3}0.000{col 54}{space 4}-.9328955{col 67}{space 3}-.4186411
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0411512{col 26}{space 2} .0331926{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.0239051{col 67}{space 3} .1062075
{txt}{space 9}gop {c |}{col 14}{res}{space 2}-.0666182{col 26}{space 2} .1415228{col 37}{space 1}   -0.47{col 46}{space 3}0.638{col 54}{space 4}-.3439978{col 67}{space 3} .2107613
{txt}{space 7}white {c |}{col 14}{res}{space 2} .4431485{col 26}{space 2} .1554122{col 37}{space 1}    2.85{col 46}{space 3}0.004{col 54}{space 4} .1385461{col 67}{space 3} .7477508
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0312013{col 26}{space 2} .0517495{col 37}{space 1}    0.60{col 46}{space 3}0.547{col 54}{space 4}-.0702259{col 67}{space 3} .1326285
{txt}gov_ideology {c |}{col 14}{res}{space 2} .0857511{col 26}{space 2} .0285611{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .0297724{col 67}{space 3} .1417298
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1683779{col 26}{space 2} .3494116{col 37}{space 1}   -0.48{col 46}{space 3}0.630{col 54}{space 4} -.853212{col 67}{space 3} .5164562
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. by gender age gop white education, sort : tabulate treatment

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        8.33        8.33
{txt}          2 {c |}{res}          3       25.00       33.33
{txt}          4 {c |}{res}          1        8.33       41.67
{txt}          5 {c |}{res}          1        8.33       50.00
{txt}          6 {c |}{res}          3       25.00       75.00
{txt}          7 {c |}{res}          2       16.67       91.67
{txt}         10 {c |}{res}          1        8.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       11.11       11.11
{txt}          2 {c |}{res}          2       11.11       22.22
{txt}          3 {c |}{res}          1        5.56       27.78
{txt}          4 {c |}{res}          4       22.22       50.00
{txt}          5 {c |}{res}          1        5.56       55.56
{txt}          6 {c |}{res}          1        5.56       61.11
{txt}          7 {c |}{res}          3       16.67       77.78
{txt}          8 {c |}{res}          2       11.11       88.89
{txt}          9 {c |}{res}          2       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         18      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       20.00       20.00
{txt}          4 {c |}{res}          1       20.00       40.00
{txt}          7 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       33.33       33.33
{txt}          3 {c |}{res}          1       33.33       66.67
{txt}          4 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1        6.67        6.67
{txt}          4 {c |}{res}          5       33.33       40.00
{txt}          5 {c |}{res}          2       13.33       53.33
{txt}          6 {c |}{res}          2       13.33       66.67
{txt}          7 {c |}{res}          1        6.67       73.33
{txt}          8 {c |}{res}          3       20.00       93.33
{txt}          9 {c |}{res}          1        6.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         15      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        4.76        4.76
{txt}          2 {c |}{res}          2        9.52       14.29
{txt}          3 {c |}{res}          1        4.76       19.05
{txt}          4 {c |}{res}          4       19.05       38.10
{txt}          5 {c |}{res}          3       14.29       52.38
{txt}          6 {c |}{res}          3       14.29       66.67
{txt}          7 {c |}{res}          2        9.52       76.19
{txt}          8 {c |}{res}          1        4.76       80.95
{txt}         10 {c |}{res}          4       19.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         21      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       50.00       50.00
{txt}          8 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       22.22       22.22
{txt}          3 {c |}{res}          1       11.11       33.33
{txt}          5 {c |}{res}          1       11.11       44.44
{txt}          6 {c |}{res}          1       11.11       55.56
{txt}          7 {c |}{res}          2       22.22       77.78
{txt}          9 {c |}{res}          1       11.11       88.89
{txt}         10 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       50.00       50.00
{txt}          8 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       25.00       25.00
{txt}          2 {c |}{res}          1       25.00       50.00
{txt}          6 {c |}{res}          1       25.00       75.00
{txt}          7 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          2 {c |}{res}          2       22.22       33.33
{txt}          3 {c |}{res}          1       11.11       44.44
{txt}          5 {c |}{res}          1       11.11       55.56
{txt}          6 {c |}{res}          2       22.22       77.78
{txt}          9 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 2, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       25.00       25.00
{txt}          2 {c |}{res}          1       25.00       50.00
{txt}          7 {c |}{res}          1       25.00       75.00
{txt}          8 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          3 {c |}{res}          1       14.29       28.57
{txt}          5 {c |}{res}          1       14.29       42.86
{txt}          7 {c |}{res}          1       14.29       57.14
{txt}          8 {c |}{res}          1       14.29       71.43
{txt}          9 {c |}{res}          1       14.29       85.71
{txt}         10 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          4 {c |}{res}          2       22.22       33.33
{txt}          7 {c |}{res}          2       22.22       55.56
{txt}          8 {c |}{res}          2       22.22       77.78
{txt}          9 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1       20.00       20.00
{txt}          7 {c |}{res}          1       20.00       40.00
{txt}          9 {c |}{res}          2       40.00       80.00
{txt}         10 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       37.50       37.50
{txt}          4 {c |}{res}          1       12.50       50.00
{txt}          5 {c |}{res}          1       12.50       62.50
{txt}          6 {c |}{res}          1       12.50       75.00
{txt}          7 {c |}{res}          1       12.50       87.50
{txt}          9 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          2 {c |}{res}          1       14.29       28.57
{txt}          3 {c |}{res}          1       14.29       42.86
{txt}          4 {c |}{res}          1       14.29       57.14
{txt}          7 {c |}{res}          1       14.29       71.43
{txt}          9 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        9.09        9.09
{txt}          3 {c |}{res}          1        9.09       18.18
{txt}          4 {c |}{res}          1        9.09       27.27
{txt}          5 {c |}{res}          3       27.27       54.55
{txt}          6 {c |}{res}          2       18.18       72.73
{txt}          8 {c |}{res}          1        9.09       81.82
{txt}          9 {c |}{res}          2       18.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       13.04       13.04
{txt}          3 {c |}{res}          2        8.70       21.74
{txt}          4 {c |}{res}          3       13.04       34.78
{txt}          6 {c |}{res}          3       13.04       47.83
{txt}          7 {c |}{res}          3       13.04       60.87
{txt}          8 {c |}{res}          1        4.35       65.22
{txt}          9 {c |}{res}          5       21.74       86.96
{txt}         10 {c |}{res}          3       13.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         23      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       14.29       14.29
{txt}          3 {c |}{res}          3       21.43       35.71
{txt}          5 {c |}{res}          2       14.29       50.00
{txt}          6 {c |}{res}          2       14.29       64.29
{txt}          7 {c |}{res}          2       14.29       78.57
{txt}          8 {c |}{res}          1        7.14       85.71
{txt}          9 {c |}{res}          1        7.14       92.86
{txt}         10 {c |}{res}          1        7.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         14      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          4       14.81       14.81
{txt}          2 {c |}{res}          3       11.11       25.93
{txt}          3 {c |}{res}          2        7.41       33.33
{txt}          4 {c |}{res}          2        7.41       40.74
{txt}          5 {c |}{res}          2        7.41       48.15
{txt}          6 {c |}{res}          7       25.93       74.07
{txt}          7 {c |}{res}          2        7.41       81.48
{txt}          8 {c |}{res}          2        7.41       88.89
{txt}          9 {c |}{res}          2        7.41       96.30
{txt}         10 {c |}{res}          1        3.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         27      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          4 {c |}{res}          3       33.33       44.44
{txt}          5 {c |}{res}          1       11.11       55.56
{txt}          8 {c |}{res}          2       22.22       77.78
{txt}         10 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          3 {c |}{res}          1       14.29       28.57
{txt}          4 {c |}{res}          1       14.29       42.86
{txt}          8 {c |}{res}          2       28.57       71.43
{txt}          9 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        9.09        9.09
{txt}          2 {c |}{res}          1        9.09       18.18
{txt}          3 {c |}{res}          3       27.27       45.45
{txt}          4 {c |}{res}          2       18.18       63.64
{txt}          7 {c |}{res}          2       18.18       81.82
{txt}          8 {c |}{res}          1        9.09       90.91
{txt}          9 {c |}{res}          1        9.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       50.00       50.00
{txt}          4 {c |}{res}          1       16.67       66.67
{txt}          5 {c |}{res}          1       16.67       83.33
{txt}          8 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          2 {c |}{res}          1       14.29       28.57
{txt}          4 {c |}{res}          1       14.29       42.86
{txt}          5 {c |}{res}          1       14.29       57.14
{txt}          6 {c |}{res}          2       28.57       85.71
{txt}          7 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 3, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       33.33       33.33
{txt}          4 {c |}{res}          1       33.33       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          2       40.00       60.00
{txt}          8 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       20.00       20.00
{txt}          3 {c |}{res}          1       10.00       30.00
{txt}          4 {c |}{res}          1       10.00       40.00
{txt}          5 {c |}{res}          1       10.00       50.00
{txt}          7 {c |}{res}          1       10.00       60.00
{txt}          8 {c |}{res}          2       20.00       80.00
{txt}          9 {c |}{res}          2       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       40.00       40.00
{txt}          3 {c |}{res}          1       20.00       60.00
{txt}          5 {c |}{res}          1       20.00       80.00
{txt}          8 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          3 {c |}{res}          1       20.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          7 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       12.50       12.50
{txt}          3 {c |}{res}          1       12.50       25.00
{txt}          4 {c |}{res}          1       12.50       37.50
{txt}          5 {c |}{res}          1       12.50       50.00
{txt}          7 {c |}{res}          1       12.50       62.50
{txt}          9 {c |}{res}          1       12.50       75.00
{txt}         10 {c |}{res}          2       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       20.00       20.00
{txt}          2 {c |}{res}          1        6.67       26.67
{txt}          3 {c |}{res}          2       13.33       40.00
{txt}          4 {c |}{res}          2       13.33       53.33
{txt}          5 {c |}{res}          2       13.33       66.67
{txt}          6 {c |}{res}          1        6.67       73.33
{txt}          9 {c |}{res}          2       13.33       86.67
{txt}         10 {c |}{res}          2       13.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         15      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          5       20.83       20.83
{txt}          2 {c |}{res}          5       20.83       41.67
{txt}          3 {c |}{res}          2        8.33       50.00
{txt}          4 {c |}{res}          1        4.17       54.17
{txt}          5 {c |}{res}          1        4.17       58.33
{txt}          6 {c |}{res}          2        8.33       66.67
{txt}          7 {c |}{res}          3       12.50       79.17
{txt}          8 {c |}{res}          2        8.33       87.50
{txt}          9 {c |}{res}          1        4.17       91.67
{txt}         10 {c |}{res}          2        8.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         24      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       14.29       14.29
{txt}          5 {c |}{res}          1       14.29       28.57
{txt}          6 {c |}{res}          1       14.29       42.86
{txt}          7 {c |}{res}          1       14.29       57.14
{txt}          8 {c |}{res}          2       28.57       85.71
{txt}         10 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       11.11       11.11
{txt}          2 {c |}{res}          3       11.11       22.22
{txt}          3 {c |}{res}          3       11.11       33.33
{txt}          4 {c |}{res}          4       14.81       48.15
{txt}          5 {c |}{res}          3       11.11       59.26
{txt}          6 {c |}{res}          3       11.11       70.37
{txt}          7 {c |}{res}          3       11.11       81.48
{txt}          8 {c |}{res}          1        3.70       85.19
{txt}          9 {c |}{res}          1        3.70       88.89
{txt}         10 {c |}{res}          3       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         27      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       10.00       10.00
{txt}          3 {c |}{res}          2       20.00       30.00
{txt}          4 {c |}{res}          1       10.00       40.00
{txt}          5 {c |}{res}          1       10.00       50.00
{txt}          7 {c |}{res}          1       10.00       60.00
{txt}          8 {c |}{res}          2       20.00       80.00
{txt}          9 {c |}{res}          1       10.00       90.00
{txt}         10 {c |}{res}          1       10.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       50.00       50.00
{txt}         10 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       25.00       25.00
{txt}          4 {c |}{res}          2       25.00       50.00
{txt}          7 {c |}{res}          1       12.50       62.50
{txt}          8 {c |}{res}          2       25.00       87.50
{txt}         10 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          2       40.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          7 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       20.00       20.00
{txt}          3 {c |}{res}          2       40.00       60.00
{txt}          5 {c |}{res}          1       20.00       80.00
{txt}          8 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       15.38       15.38
{txt}          2 {c |}{res}          2       15.38       30.77
{txt}          3 {c |}{res}          1        7.69       38.46
{txt}          5 {c |}{res}          1        7.69       46.15
{txt}          6 {c |}{res}          1        7.69       53.85
{txt}          7 {c |}{res}          2       15.38       69.23
{txt}          8 {c |}{res}          2       15.38       84.62
{txt}          9 {c |}{res}          1        7.69       92.31
{txt}         10 {c |}{res}          1        7.69      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         13      100.00

{txt}{hline}
-> gender = 0, age = 4, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       33.33       33.33
{txt}          9 {c |}{res}          1       33.33       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       22.22       22.22
{txt}          3 {c |}{res}          2       22.22       44.44
{txt}          4 {c |}{res}          1       11.11       55.56
{txt}          7 {c |}{res}          1       11.11       66.67
{txt}          8 {c |}{res}          1       11.11       77.78
{txt}          9 {c |}{res}          1       11.11       88.89
{txt}         10 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       50.00       50.00
{txt}          8 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          2       40.00       60.00
{txt}          6 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       25.00       25.00
{txt}          8 {c |}{res}          1       25.00       50.00
{txt}          9 {c |}{res}          2       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        9.09        9.09
{txt}          2 {c |}{res}          2       18.18       27.27
{txt}          3 {c |}{res}          1        9.09       36.36
{txt}          5 {c |}{res}          3       27.27       63.64
{txt}          7 {c |}{res}          1        9.09       72.73
{txt}         10 {c |}{res}          3       27.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       11.11       11.11
{txt}          3 {c |}{res}          1        5.56       16.67
{txt}          4 {c |}{res}          1        5.56       22.22
{txt}          5 {c |}{res}          2       11.11       33.33
{txt}          6 {c |}{res}          3       16.67       50.00
{txt}          7 {c |}{res}          3       16.67       66.67
{txt}          8 {c |}{res}          3       16.67       83.33
{txt}          9 {c |}{res}          1        5.56       88.89
{txt}         10 {c |}{res}          2       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         18      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       16.67       16.67
{txt}          2 {c |}{res}          1       16.67       33.33
{txt}          5 {c |}{res}          2       33.33       66.67
{txt}          6 {c |}{res}          1       16.67       83.33
{txt}         10 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        7.69        7.69
{txt}          3 {c |}{res}          4       30.77       38.46
{txt}          4 {c |}{res}          1        7.69       46.15
{txt}          7 {c |}{res}          1        7.69       53.85
{txt}          8 {c |}{res}          3       23.08       76.92
{txt}          9 {c |}{res}          2       15.38       92.31
{txt}         10 {c |}{res}          1        7.69      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         13      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       20.00       20.00
{txt}          2 {c |}{res}          1       20.00       40.00
{txt}          5 {c |}{res}          1       20.00       60.00
{txt}          9 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          2 {c |}{res}          2       22.22       33.33
{txt}          3 {c |}{res}          1       11.11       44.44
{txt}          6 {c |}{res}          1       11.11       55.56
{txt}          7 {c |}{res}          1       11.11       66.67
{txt}          9 {c |}{res}          1       11.11       77.78
{txt}         10 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       16.67       16.67
{txt}          5 {c |}{res}          1       16.67       33.33
{txt}          6 {c |}{res}          1       16.67       50.00
{txt}          8 {c |}{res}          1       16.67       66.67
{txt}          9 {c |}{res}          2       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       33.33       33.33
{txt}          2 {c |}{res}          1       33.33       66.67
{txt}          3 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          2       25.00       25.00
{txt}          5 {c |}{res}          2       25.00       50.00
{txt}          6 {c |}{res}          1       12.50       62.50
{txt}          8 {c |}{res}          1       12.50       75.00
{txt}          9 {c |}{res}          1       12.50       87.50
{txt}         10 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 5, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       22.22       22.22
{txt}          2 {c |}{res}          1       11.11       33.33
{txt}          4 {c |}{res}          3       33.33       66.67
{txt}          7 {c |}{res}          1       11.11       77.78
{txt}          9 {c |}{res}          1       11.11       88.89
{txt}         10 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       33.33       33.33
{txt}          5 {c |}{res}          1       33.33       66.67
{txt}          9 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       25.00       25.00
{txt}          5 {c |}{res}          1       25.00       50.00
{txt}          6 {c |}{res}          2       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       25.00       25.00
{txt}          4 {c |}{res}          1       25.00       50.00
{txt}          5 {c |}{res}          1       25.00       75.00
{txt}          8 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       16.67       16.67
{txt}          2 {c |}{res}          1        8.33       25.00
{txt}          3 {c |}{res}          1        8.33       33.33
{txt}          4 {c |}{res}          1        8.33       41.67
{txt}          6 {c |}{res}          1        8.33       50.00
{txt}          8 {c |}{res}          1        8.33       58.33
{txt}          9 {c |}{res}          1        8.33       66.67
{txt}         10 {c |}{res}          4       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       12.50       12.50
{txt}          3 {c |}{res}          3       18.75       31.25
{txt}          4 {c |}{res}          1        6.25       37.50
{txt}          6 {c |}{res}          2       12.50       50.00
{txt}          7 {c |}{res}          2       12.50       62.50
{txt}          8 {c |}{res}          3       18.75       81.25
{txt}          9 {c |}{res}          1        6.25       87.50
{txt}         10 {c |}{res}          2       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         16      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          1       20.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1        9.09        9.09
{txt}          4 {c |}{res}          1        9.09       18.18
{txt}          5 {c |}{res}          3       27.27       45.45
{txt}          7 {c |}{res}          1        9.09       54.55
{txt}          8 {c |}{res}          1        9.09       63.64
{txt}          9 {c |}{res}          4       36.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       28.57       28.57
{txt}          2 {c |}{res}          1       14.29       42.86
{txt}          4 {c |}{res}          1       14.29       57.14
{txt}          6 {c |}{res}          2       28.57       85.71
{txt}          8 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       11.11       11.11
{txt}          3 {c |}{res}          1       11.11       22.22
{txt}          7 {c |}{res}          1       11.11       33.33
{txt}          8 {c |}{res}          2       22.22       55.56
{txt}          9 {c |}{res}          3       33.33       88.89
{txt}         10 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          2       15.38       15.38
{txt}          3 {c |}{res}          4       30.77       46.15
{txt}          4 {c |}{res}          1        7.69       53.85
{txt}          5 {c |}{res}          1        7.69       61.54
{txt}          8 {c |}{res}          2       15.38       76.92
{txt}          9 {c |}{res}          3       23.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         13      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          2       40.00       40.00
{txt}          5 {c |}{res}          2       40.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       22.22       22.22
{txt}          4 {c |}{res}          2       22.22       44.44
{txt}          5 {c |}{res}          1       11.11       55.56
{txt}          6 {c |}{res}          1       11.11       66.67
{txt}          7 {c |}{res}          2       22.22       88.89
{txt}          8 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 0, age = 6, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       50.00       50.00
{txt}          9 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          2       50.00       50.00
{txt}          6 {c |}{res}          1       25.00       75.00
{txt}          9 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       12.50       12.50
{txt}          2 {c |}{res}          2       25.00       37.50
{txt}          4 {c |}{res}          2       25.00       62.50
{txt}          6 {c |}{res}          1       12.50       75.00
{txt}          8 {c |}{res}          2       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1       25.00       25.00
{txt}          7 {c |}{res}          3       75.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       25.00       25.00
{txt}          4 {c |}{res}          1       25.00       50.00
{txt}          8 {c |}{res}          1       25.00       75.00
{txt}          9 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        8.33        8.33
{txt}          3 {c |}{res}          2       16.67       25.00
{txt}          4 {c |}{res}          1        8.33       33.33
{txt}          5 {c |}{res}          2       16.67       50.00
{txt}          6 {c |}{res}          2       16.67       66.67
{txt}          7 {c |}{res}          1        8.33       75.00
{txt}          8 {c |}{res}          2       16.67       91.67
{txt}         10 {c |}{res}          1        8.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1        7.69        7.69
{txt}          3 {c |}{res}          1        7.69       15.38
{txt}          4 {c |}{res}          3       23.08       38.46
{txt}          6 {c |}{res}          1        7.69       46.15
{txt}          7 {c |}{res}          1        7.69       53.85
{txt}          8 {c |}{res}          3       23.08       76.92
{txt}          9 {c |}{res}          1        7.69       84.62
{txt}         10 {c |}{res}          2       15.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         13      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          4 {c |}{res}          1       14.29       28.57
{txt}          5 {c |}{res}          2       28.57       57.14
{txt}          7 {c |}{res}          1       14.29       71.43
{txt}         10 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          2       40.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          7 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          1       20.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       12.50       12.50
{txt}          2 {c |}{res}          1       12.50       25.00
{txt}          4 {c |}{res}          1       12.50       37.50
{txt}          6 {c |}{res}          2       25.00       62.50
{txt}          8 {c |}{res}          1       12.50       75.00
{txt}         10 {c |}{res}          2       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 0, age = 7, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       42.86       42.86
{txt}          5 {c |}{res}          2       28.57       71.43
{txt}          9 {c |}{res}          1       14.29       85.71
{txt}         10 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       50.00       50.00
{txt}          6 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 0, age = 8, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        8.33        8.33
{txt}          3 {c |}{res}          1        8.33       16.67
{txt}          4 {c |}{res}          1        8.33       25.00
{txt}          6 {c |}{res}          4       33.33       58.33
{txt}          9 {c |}{res}          3       25.00       83.33
{txt}         10 {c |}{res}          2       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       27.27       27.27
{txt}          3 {c |}{res}          1        9.09       36.36
{txt}          4 {c |}{res}          1        9.09       45.45
{txt}          6 {c |}{res}          1        9.09       54.55
{txt}          9 {c |}{res}          3       27.27       81.82
{txt}         10 {c |}{res}          2       18.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       50.00       50.00
{txt}         10 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          3       42.86       42.86
{txt}          3 {c |}{res}          1       14.29       57.14
{txt}          4 {c |}{res}          1       14.29       71.43
{txt}          5 {c |}{res}          1       14.29       85.71
{txt}         10 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       14.29       14.29
{txt}          3 {c |}{res}          2       28.57       42.86
{txt}          4 {c |}{res}          1       14.29       57.14
{txt}          7 {c |}{res}          1       14.29       71.43
{txt}          8 {c |}{res}          1       14.29       85.71
{txt}          9 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          2       66.67       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         10 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         10 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       33.33       33.33
{txt}          8 {c |}{res}          1       33.33       66.67
{txt}          9 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       20.00       20.00
{txt}          2 {c |}{res}          1       10.00       30.00
{txt}          5 {c |}{res}          2       20.00       50.00
{txt}          6 {c |}{res}          1       10.00       60.00
{txt}          8 {c |}{res}          2       20.00       80.00
{txt}          9 {c |}{res}          1       10.00       90.00
{txt}         10 {c |}{res}          1       10.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          7 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 2, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       14.29       14.29
{txt}          5 {c |}{res}          1       14.29       28.57
{txt}          6 {c |}{res}          1       14.29       42.86
{txt}          8 {c |}{res}          1       14.29       57.14
{txt}          9 {c |}{res}          1       14.29       71.43
{txt}         10 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          2       28.57       28.57
{txt}          6 {c |}{res}          2       28.57       57.14
{txt}          8 {c |}{res}          1       14.29       71.43
{txt}          9 {c |}{res}          1       14.29       85.71
{txt}         10 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       16.67       16.67
{txt}          4 {c |}{res}          2       33.33       50.00
{txt}          8 {c |}{res}          2       33.33       83.33
{txt}         10 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       25.00       25.00
{txt}          3 {c |}{res}          1       25.00       50.00
{txt}          4 {c |}{res}          1       25.00       75.00
{txt}          9 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       20.00       20.00
{txt}          2 {c |}{res}          2       20.00       40.00
{txt}          7 {c |}{res}          2       20.00       60.00
{txt}          8 {c |}{res}          3       30.00       90.00
{txt}         10 {c |}{res}          1       10.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       25.00       25.00
{txt}          5 {c |}{res}          1       25.00       50.00
{txt}          6 {c |}{res}          1       25.00       75.00
{txt}         10 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       12.50       12.50
{txt}          4 {c |}{res}          4       50.00       62.50
{txt}          5 {c |}{res}          2       25.00       87.50
{txt}          7 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       20.00       20.00
{txt}          2 {c |}{res}          1       10.00       30.00
{txt}          4 {c |}{res}          1       10.00       40.00
{txt}          6 {c |}{res}          1       10.00       50.00
{txt}          7 {c |}{res}          1       10.00       60.00
{txt}          8 {c |}{res}          1       10.00       70.00
{txt}          9 {c |}{res}          2       20.00       90.00
{txt}         10 {c |}{res}          1       10.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       10.00       10.00
{txt}          2 {c |}{res}          1       10.00       20.00
{txt}          4 {c |}{res}          2       20.00       40.00
{txt}          5 {c |}{res}          1       10.00       50.00
{txt}          7 {c |}{res}          3       30.00       80.00
{txt}          8 {c |}{res}          2       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3        9.09        9.09
{txt}          2 {c |}{res}          3        9.09       18.18
{txt}          3 {c |}{res}          5       15.15       33.33
{txt}          4 {c |}{res}          2        6.06       39.39
{txt}          5 {c |}{res}          5       15.15       54.55
{txt}          6 {c |}{res}          6       18.18       72.73
{txt}          7 {c |}{res}          3        9.09       81.82
{txt}          8 {c |}{res}          2        6.06       87.88
{txt}          9 {c |}{res}          1        3.03       90.91
{txt}         10 {c |}{res}          3        9.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         33      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        6.67        6.67
{txt}          2 {c |}{res}          1        6.67       13.33
{txt}          3 {c |}{res}          3       20.00       33.33
{txt}          4 {c |}{res}          1        6.67       40.00
{txt}          5 {c |}{res}          1        6.67       46.67
{txt}          7 {c |}{res}          2       13.33       60.00
{txt}          8 {c |}{res}          5       33.33       93.33
{txt}          9 {c |}{res}          1        6.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         15      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       50.00       50.00
{txt}          9 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       20.00       20.00
{txt}          4 {c |}{res}          3       60.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       50.00       50.00
{txt}          4 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       25.00       25.00
{txt}          7 {c |}{res}          2       50.00       75.00
{txt}         10 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       16.67       16.67
{txt}          2 {c |}{res}          2       33.33       50.00
{txt}          6 {c |}{res}          1       16.67       66.67
{txt}          9 {c |}{res}          2       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          1       20.00       40.00
{txt}          6 {c |}{res}          2       40.00       80.00
{txt}         10 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1        7.14        7.14
{txt}          4 {c |}{res}          2       14.29       21.43
{txt}          5 {c |}{res}          1        7.14       28.57
{txt}          6 {c |}{res}          3       21.43       50.00
{txt}          7 {c |}{res}          1        7.14       57.14
{txt}          8 {c |}{res}          4       28.57       85.71
{txt}          9 {c |}{res}          1        7.14       92.86
{txt}         10 {c |}{res}          1        7.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         14      100.00

{txt}{hline}
-> gender = 1, age = 3, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       20.00       20.00
{txt}          7 {c |}{res}          1       20.00       40.00
{txt}          9 {c |}{res}          2       40.00       80.00
{txt}         10 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       33.33       33.33
{txt}          5 {c |}{res}          1       33.33       66.67
{txt}          9 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       25.00       25.00
{txt}          4 {c |}{res}          2       50.00       75.00
{txt}          9 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       16.67       16.67
{txt}          3 {c |}{res}          1       16.67       33.33
{txt}          4 {c |}{res}          1       16.67       50.00
{txt}          6 {c |}{res}          1       16.67       66.67
{txt}          8 {c |}{res}          1       16.67       83.33
{txt}          9 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          2       66.67       66.67
{txt}          8 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       11.11       11.11
{txt}          3 {c |}{res}          1       11.11       22.22
{txt}          4 {c |}{res}          2       22.22       44.44
{txt}          5 {c |}{res}          1       11.11       55.56
{txt}          7 {c |}{res}          1       11.11       66.67
{txt}          8 {c |}{res}          1       11.11       77.78
{txt}         10 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       14.29       14.29
{txt}          2 {c |}{res}          2       14.29       28.57
{txt}          3 {c |}{res}          4       28.57       57.14
{txt}          4 {c |}{res}          1        7.14       64.29
{txt}          5 {c |}{res}          1        7.14       71.43
{txt}          6 {c |}{res}          1        7.14       78.57
{txt}          7 {c |}{res}          1        7.14       85.71
{txt}          9 {c |}{res}          1        7.14       92.86
{txt}         10 {c |}{res}          1        7.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         14      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          2 {c |}{res}          1       14.29       28.57
{txt}          3 {c |}{res}          2       28.57       57.14
{txt}          4 {c |}{res}          1       14.29       71.43
{txt}          8 {c |}{res}          1       14.29       85.71
{txt}          9 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2        7.41        7.41
{txt}          2 {c |}{res}          4       14.81       22.22
{txt}          3 {c |}{res}          3       11.11       33.33
{txt}          4 {c |}{res}          4       14.81       48.15
{txt}          6 {c |}{res}          3       11.11       59.26
{txt}          7 {c |}{res}          2        7.41       66.67
{txt}          8 {c |}{res}          1        3.70       70.37
{txt}          9 {c |}{res}          5       18.52       88.89
{txt}         10 {c |}{res}          3       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         27      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          3       10.71       10.71
{txt}          2 {c |}{res}          4       14.29       25.00
{txt}          3 {c |}{res}          3       10.71       35.71
{txt}          4 {c |}{res}          2        7.14       42.86
{txt}          5 {c |}{res}          3       10.71       53.57
{txt}          6 {c |}{res}          4       14.29       67.86
{txt}          7 {c |}{res}          2        7.14       75.00
{txt}          8 {c |}{res}          3       10.71       85.71
{txt}          9 {c |}{res}          2        7.14       92.86
{txt}         10 {c |}{res}          2        7.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         28      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        9.09        9.09
{txt}          3 {c |}{res}          3       27.27       36.36
{txt}          4 {c |}{res}          1        9.09       45.45
{txt}          6 {c |}{res}          1        9.09       54.55
{txt}          7 {c |}{res}          1        9.09       63.64
{txt}          8 {c |}{res}          1        9.09       72.73
{txt}          9 {c |}{res}          3       27.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         11      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       12.50       12.50
{txt}          2 {c |}{res}          1       12.50       25.00
{txt}          4 {c |}{res}          1       12.50       37.50
{txt}          5 {c |}{res}          1       12.50       50.00
{txt}          7 {c |}{res}          2       25.00       75.00
{txt}          8 {c |}{res}          1       12.50       87.50
{txt}          9 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          2       50.00       50.00
{txt}          5 {c |}{res}          1       25.00       75.00
{txt}          8 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       13.33       13.33
{txt}          3 {c |}{res}          1        6.67       20.00
{txt}          4 {c |}{res}          2       13.33       33.33
{txt}          5 {c |}{res}          2       13.33       46.67
{txt}          6 {c |}{res}          1        6.67       53.33
{txt}          7 {c |}{res}          3       20.00       73.33
{txt}          8 {c |}{res}          3       20.00       93.33
{txt}          9 {c |}{res}          1        6.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         15      100.00

{txt}{hline}
-> gender = 1, age = 4, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2        6.90        6.90
{txt}          2 {c |}{res}          3       10.34       17.24
{txt}          3 {c |}{res}          3       10.34       27.59
{txt}          4 {c |}{res}          4       13.79       41.38
{txt}          5 {c |}{res}          2        6.90       48.28
{txt}          6 {c |}{res}          1        3.45       51.72
{txt}          7 {c |}{res}          5       17.24       68.97
{txt}          8 {c |}{res}          3       10.34       79.31
{txt}          9 {c |}{res}          4       13.79       93.10
{txt}         10 {c |}{res}          2        6.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         29      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       50.00       50.00
{txt}          8 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       33.33       33.33
{txt}          3 {c |}{res}          1       16.67       50.00
{txt}          4 {c |}{res}          2       33.33       83.33
{txt}          5 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       50.00       50.00
{txt}          9 {c |}{res}          1       25.00       75.00
{txt}         10 {c |}{res}          1       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          4      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       16.67       16.67
{txt}          2 {c |}{res}          1       16.67       33.33
{txt}          3 {c |}{res}          2       33.33       66.67
{txt}          7 {c |}{res}          1       16.67       83.33
{txt}         10 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       16.67       16.67
{txt}          2 {c |}{res}          1       16.67       33.33
{txt}          3 {c |}{res}          1       16.67       50.00
{txt}          7 {c |}{res}          2       33.33       83.33
{txt}         10 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       33.33       33.33
{txt}          4 {c |}{res}          1       33.33       66.67
{txt}          6 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       12.50       12.50
{txt}          3 {c |}{res}          2       25.00       37.50
{txt}          6 {c |}{res}          1       12.50       50.00
{txt}          8 {c |}{res}          2       25.00       75.00
{txt}          9 {c |}{res}          1       12.50       87.50
{txt}         10 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1        8.33        8.33
{txt}          4 {c |}{res}          1        8.33       16.67
{txt}          7 {c |}{res}          2       16.67       33.33
{txt}          8 {c |}{res}          2       16.67       50.00
{txt}          9 {c |}{res}          3       25.00       75.00
{txt}         10 {c |}{res}          3       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         10 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1       50.00       50.00
{txt}          9 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          3 {c |}{res}          1       20.00       40.00
{txt}          5 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       33.33       33.33
{txt}          8 {c |}{res}          2       66.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          2 {c |}{res}          1       11.11       22.22
{txt}          3 {c |}{res}          2       22.22       44.44
{txt}          4 {c |}{res}          1       11.11       55.56
{txt}          5 {c |}{res}          1       11.11       66.67
{txt}          7 {c |}{res}          1       11.11       77.78
{txt}          8 {c |}{res}          2       22.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 1, age = 5, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1        7.69        7.69
{txt}          4 {c |}{res}          3       23.08       30.77
{txt}          5 {c |}{res}          2       15.38       46.15
{txt}          6 {c |}{res}          1        7.69       53.85
{txt}          7 {c |}{res}          1        7.69       61.54
{txt}          9 {c |}{res}          2       15.38       76.92
{txt}         10 {c |}{res}          3       23.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         13      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       33.33       33.33
{txt}          6 {c |}{res}          1       33.33       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 0, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          4 {c |}{res}          1       20.00       40.00
{txt}          7 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          1       20.00       80.00
{txt}          9 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         10 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       12.50       12.50
{txt}          3 {c |}{res}          1       12.50       25.00
{txt}          4 {c |}{res}          2       25.00       50.00
{txt}          7 {c |}{res}          2       25.00       75.00
{txt}          8 {c |}{res}          1       12.50       87.50
{txt}         10 {c |}{res}          1       12.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          8      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       10.00       10.00
{txt}          2 {c |}{res}          1       10.00       20.00
{txt}          3 {c |}{res}          1       10.00       30.00
{txt}          4 {c |}{res}          1       10.00       40.00
{txt}          5 {c |}{res}          3       30.00       70.00
{txt}          7 {c |}{res}          1       10.00       80.00
{txt}          8 {c |}{res}          1       10.00       90.00
{txt}          9 {c |}{res}          1       10.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         10      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       11.11       11.11
{txt}          2 {c |}{res}          1       11.11       22.22
{txt}          4 {c |}{res}          1       11.11       33.33
{txt}          6 {c |}{res}          1       11.11       44.44
{txt}          7 {c |}{res}          1       11.11       55.56
{txt}          8 {c |}{res}          2       22.22       77.78
{txt}          9 {c |}{res}          1       11.11       88.89
{txt}         10 {c |}{res}          1       11.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          9      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       14.29       14.29
{txt}          2 {c |}{res}          1        7.14       21.43
{txt}          3 {c |}{res}          2       14.29       35.71
{txt}          4 {c |}{res}          4       28.57       64.29
{txt}          5 {c |}{res}          1        7.14       71.43
{txt}          7 {c |}{res}          2       14.29       85.71
{txt}          8 {c |}{res}          1        7.14       92.86
{txt}          9 {c |}{res}          1        7.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         14      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       16.67       16.67
{txt}          5 {c |}{res}          4       66.67       83.33
{txt}          6 {c |}{res}          1       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          6      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          8 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          2      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          2 {c |}{res}          1       14.29       28.57
{txt}          5 {c |}{res}          2       28.57       57.14
{txt}          8 {c |}{res}          1       14.29       71.43
{txt}          9 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          2       66.67       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        8.33        8.33
{txt}          3 {c |}{res}          4       33.33       41.67
{txt}          4 {c |}{res}          2       16.67       58.33
{txt}          7 {c |}{res}          1        8.33       66.67
{txt}          8 {c |}{res}          1        8.33       75.00
{txt}          9 {c |}{res}          1        8.33       83.33
{txt}         10 {c |}{res}          2       16.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 1, age = 6, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       50.00       50.00
{txt}          8 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 0, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 0, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       33.33       33.33
{txt}          3 {c |}{res}          1       33.33       66.67
{txt}         10 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       33.33       33.33
{txt}          6 {c |}{res}          1       33.33       66.67
{txt}          7 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       14.29       14.29
{txt}          2 {c |}{res}          1       14.29       28.57
{txt}          3 {c |}{res}          1       14.29       42.86
{txt}          7 {c |}{res}          2       28.57       71.43
{txt}         10 {c |}{res}          2       28.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          8 {c |}{res}          1       20.00       40.00
{txt}          9 {c |}{res}          1       20.00       60.00
{txt}         10 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1        8.33        8.33
{txt}          3 {c |}{res}          1        8.33       16.67
{txt}          5 {c |}{res}          3       25.00       41.67
{txt}          6 {c |}{res}          2       16.67       58.33
{txt}          9 {c |}{res}          2       16.67       75.00
{txt}         10 {c |}{res}          3       25.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         12      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       28.57       28.57
{txt}          2 {c |}{res}          1       14.29       42.86
{txt}          4 {c |}{res}          1       14.29       57.14
{txt}          7 {c |}{res}          1       14.29       71.43
{txt}          8 {c |}{res}          1       14.29       85.71
{txt}          9 {c |}{res}          1       14.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          7      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 0, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          5 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 0, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          4 {c |}{res}          1       20.00       40.00
{txt}          5 {c |}{res}          1       20.00       60.00
{txt}          6 {c |}{res}          1       20.00       80.00
{txt}         10 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          3 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          1       20.00       40.00
{txt}          6 {c |}{res}          1       20.00       60.00
{txt}          9 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 1, education = 3

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          4 {c |}{res}          1       20.00       20.00
{txt}          5 {c |}{res}          2       40.00       60.00
{txt}          7 {c |}{res}          2       40.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          2       66.67       66.67
{txt}          5 {c |}{res}          1       33.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          3      100.00

{txt}{hline}
-> gender = 1, age = 7, gop = 1, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1       20.00       20.00
{txt}          4 {c |}{res}          1       20.00       40.00
{txt}          5 {c |}{res}          1       20.00       60.00
{txt}          8 {c |}{res}          1       20.00       80.00
{txt}         10 {c |}{res}          1       20.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          5      100.00

{txt}{hline}
-> gender = 1, age = 8, gop = 0, white = 1, education = 1

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 8, gop = 0, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          9 {c |}{res}          1       50.00       50.00
{txt}         10 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 8, gop = 0, white = 1, education = 5

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1       50.00       50.00
{txt}         10 {c |}{res}          1       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          2      100.00

{txt}{hline}
-> gender = 1, age = 8, gop = 1, white = 1, education = 2

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00

{txt}{hline}
-> gender = 1, age = 8, gop = 1, white = 1, education = 4

  treatment {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}          1      100.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}          1      100.00


{com}. mean gender age education income white, over(treatment)
{res}
{txt}Mean estimation{col 44}Number of obs{col 60}= {res}     1,346

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 23}{c |}       Mean{col 35}   Std. Err.{col 47}     [95% Con{col 60}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 3}c.gender@treatment {c |}
{space 18} 1  {c |}{col 23}{res}{space 2} .4328358{col 35}{space 2} .0429626{col 46}{space 5} .3485549{col 60}{space 3} .5171167
{txt}{space 18} 2  {c |}{col 23}{res}{space 2}       .5{col 35}{space 2} .0450835{col 46}{space 5} .4115584{col 60}{space 3} .5884416
{txt}{space 18} 3  {c |}{col 23}{res}{space 2} .4552239{col 35}{space 2} .0431813{col 46}{space 5} .3705139{col 60}{space 3} .5399339
{txt}{space 18} 4  {c |}{col 23}{res}{space 2} .4834437{col 35}{space 2} .0408024{col 46}{space 5} .4034004{col 60}{space 3} .5634871
{txt}{space 18} 5  {c |}{col 23}{res}{space 2} .4748201{col 35}{space 2} .0425088{col 46}{space 5} .3914293{col 60}{space 3} .5582109
{txt}{space 18} 6  {c |}{col 23}{res}{space 2} .4033613{col 35}{space 2} .0451608{col 46}{space 5}  .314768{col 60}{space 3} .4919547
{txt}{space 18} 7  {c |}{col 23}{res}{space 2}  .453125{col 35}{space 2} .0441724{col 46}{space 5} .3664707{col 60}{space 3} .5397793
{txt}{space 18} 8  {c |}{col 23}{res}{space 2} .4693878{col 35}{space 2} .0413027{col 46}{space 5} .3883631{col 60}{space 3} .5504124
{txt}{space 18} 9  {c |}{col 23}{res}{space 2} .4864865{col 35}{space 2} .0412242{col 46}{space 5} .4056157{col 60}{space 3} .5673573
{txt}{space 18}10  {c |}{col 23}{res}{space 2} .5245902{col 35}{space 2} .0453995{col 46}{space 5} .4355286{col 60}{space 3} .6136518
{txt}{space 21} {c |}
{space 6}c.age@treatment {c |}
{space 18} 1  {c |}{col 23}{res}{space 2} 4.246269{col 35}{space 2}  .135014{col 46}{space 5} 3.981408{col 60}{space 3}  4.51113
{txt}{space 18} 2  {c |}{col 23}{res}{space 2} 4.258065{col 35}{space 2} .1425071{col 46}{space 5} 3.978504{col 60}{space 3} 4.537625
{txt}{space 18} 3  {c |}{col 23}{res}{space 2} 4.373134{col 35}{space 2} .1256435{col 46}{space 5} 4.126656{col 60}{space 3} 4.619613
{txt}{space 18} 4  {c |}{col 23}{res}{space 2} 4.145695{col 35}{space 2} .1272558{col 46}{space 5} 3.896054{col 60}{space 3} 4.395337
{txt}{space 18} 5  {c |}{col 23}{res}{space 2} 4.517986{col 35}{space 2} .1375086{col 46}{space 5} 4.248231{col 60}{space 3}  4.78774
{txt}{space 18} 6  {c |}{col 23}{res}{space 2} 4.134454{col 35}{space 2}  .156028{col 46}{space 5} 3.828369{col 60}{space 3} 4.440538
{txt}{space 18} 7  {c |}{col 23}{res}{space 2} 4.171875{col 35}{space 2} .1416601{col 46}{space 5} 3.893976{col 60}{space 3} 4.449774
{txt}{space 18} 8  {c |}{col 23}{res}{space 2}  4.29932{col 35}{space 2} .1260183{col 46}{space 5} 4.052106{col 60}{space 3} 4.546534
{txt}{space 18} 9  {c |}{col 23}{res}{space 2} 4.371622{col 35}{space 2} .1262119{col 46}{space 5} 4.124028{col 60}{space 3} 4.619215
{txt}{space 18}10  {c |}{col 23}{res}{space 2} 4.459016{col 35}{space 2} .1522747{col 46}{space 5} 4.160295{col 60}{space 3} 4.757738
{txt}{space 21} {c |}
c.education@treatment {c |}
{space 18} 1  {c |}{col 23}{res}{space 2} 3.126866{col 35}{space 2} .1191098{col 46}{space 5} 2.893204{col 60}{space 3} 3.360527
{txt}{space 18} 2  {c |}{col 23}{res}{space 2} 2.903226{col 35}{space 2} .1256608{col 46}{space 5} 2.656713{col 60}{space 3} 3.149738
{txt}{space 18} 3  {c |}{col 23}{res}{space 2} 2.940299{col 35}{space 2} .1134839{col 46}{space 5} 2.717674{col 60}{space 3} 3.162923
{txt}{space 18} 4  {c |}{col 23}{res}{space 2} 3.006623{col 35}{space 2} .1186743{col 46}{space 5} 2.773816{col 60}{space 3} 3.239429
{txt}{space 18} 5  {c |}{col 23}{res}{space 2}  3.05036{col 35}{space 2} .1226696{col 46}{space 5} 2.809715{col 60}{space 3} 3.291004
{txt}{space 18} 6  {c |}{col 23}{res}{space 2}  2.94958{col 35}{space 2} .1198494{col 46}{space 5} 2.714468{col 60}{space 3} 3.184692
{txt}{space 18} 7  {c |}{col 23}{res}{space 2} 2.921875{col 35}{space 2} .1223184{col 46}{space 5} 2.681919{col 60}{space 3} 3.161831
{txt}{space 18} 8  {c |}{col 23}{res}{space 2} 3.115646{col 35}{space 2} .1123768{col 46}{space 5} 2.895193{col 60}{space 3} 3.336099
{txt}{space 18} 9  {c |}{col 23}{res}{space 2} 2.972973{col 35}{space 2} .1185754{col 46}{space 5}  2.74036{col 60}{space 3} 3.205586
{txt}{space 18}10  {c |}{col 23}{res}{space 2} 2.983607{col 35}{space 2} .1391828{col 46}{space 5} 2.710568{col 60}{space 3} 3.256646
{txt}{space 21} {c |}
{space 3}c.income@treatment {c |}
{space 18} 1  {c |}{col 23}{res}{space 2} 3.746269{col 35}{space 2} .1803022{col 46}{space 5} 3.392565{col 60}{space 3} 4.099973
{txt}{space 18} 2  {c |}{col 23}{res}{space 2} 3.483871{col 35}{space 2} .1819569{col 46}{space 5} 3.126921{col 60}{space 3} 3.840821
{txt}{space 18} 3  {c |}{col 23}{res}{space 2} 3.522388{col 35}{space 2} .1736536{col 46}{space 5} 3.181727{col 60}{space 3} 3.863049
{txt}{space 18} 4  {c |}{col 23}{res}{space 2} 3.410596{col 35}{space 2} .1935543{col 46}{space 5} 3.030895{col 60}{space 3} 3.790297
{txt}{space 18} 5  {c |}{col 23}{res}{space 2} 3.870504{col 35}{space 2} .1945012{col 46}{space 5} 3.488945{col 60}{space 3} 4.252062
{txt}{space 18} 6  {c |}{col 23}{res}{space 2} 3.756303{col 35}{space 2} .2070425{col 46}{space 5} 3.350141{col 60}{space 3} 4.162464
{txt}{space 18} 7  {c |}{col 23}{res}{space 2} 3.726563{col 35}{space 2} .1923471{col 46}{space 5}  3.34923{col 60}{space 3} 4.103895
{txt}{space 18} 8  {c |}{col 23}{res}{space 2} 4.122449{col 35}{space 2} .1763952{col 46}{space 5} 3.776409{col 60}{space 3} 4.468489
{txt}{space 18} 9  {c |}{col 23}{res}{space 2} 3.527027{col 35}{space 2} .1921052{col 46}{space 5} 3.150169{col 60}{space 3} 3.903885
{txt}{space 18}10  {c |}{col 23}{res}{space 2} 3.655738{col 35}{space 2} .2081429{col 46}{space 5} 3.247418{col 60}{space 3} 4.064058
{txt}{space 21} {c |}
{space 4}c.white@treatment {c |}
{space 18} 1  {c |}{col 23}{res}{space 2}  .738806{col 35}{space 2} .0380909{col 46}{space 5}  .664082{col 60}{space 3}   .81353
{txt}{space 18} 2  {c |}{col 23}{res}{space 2} .8467742{col 35}{space 2} .0324786{col 46}{space 5}   .78306{col 60}{space 3} .9104884
{txt}{space 18} 3  {c |}{col 23}{res}{space 2} .8134328{col 35}{space 2} .0337795{col 46}{space 5} .7471667{col 60}{space 3}  .879699
{txt}{space 18} 4  {c |}{col 23}{res}{space 2} .7615894{col 35}{space 2} .0347919{col 46}{space 5} .6933372{col 60}{space 3} .8298416
{txt}{space 18} 5  {c |}{col 23}{res}{space 2} .8057554{col 35}{space 2} .0336772{col 46}{space 5} .7396898{col 60}{space 3}  .871821
{txt}{space 18} 6  {c |}{col 23}{res}{space 2} .7983193{col 35}{space 2} .0369385{col 46}{space 5}  .725856{col 60}{space 3} .8707827
{txt}{space 18} 7  {c |}{col 23}{res}{space 2} .8203125{col 35}{space 2}  .034068{col 46}{space 5} .7534803{col 60}{space 3} .8871447
{txt}{space 18} 8  {c |}{col 23}{res}{space 2} .7891156{col 35}{space 2} .0337611{col 46}{space 5} .7228856{col 60}{space 3} .8553457
{txt}{space 18} 9  {c |}{col 23}{res}{space 2} .7162162{col 35}{space 2} .0371841{col 46}{space 5} .6432711{col 60}{space 3} .7891613
{txt}{space 18}10  {c |}{col 23}{res}{space 2}  .795082{col 35}{space 2} .0366947{col 46}{space 5} .7230969{col 60}{space 3}  .867067
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gender): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gender
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     41{col 35}    100{col 43} 0.4100{col 51} 0.0492{col 59}   .31262{col 69}{space 1} .5128558
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(white): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  white
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     43{col 35}    100{col 43} 0.4300{col 51} 0.0495{col 59}  .331391{col 69}{space 1} .5328663
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gov_ideology): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{err}Warning: 2% of the resampled realizations for _pm_1 are exactly identical to original value
{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gov_ideology
       {txt}Strata{res}:  10

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     46{col 35}    100{col 43} 0.4600{col 51} 0.0498{col 59} .3598434{col 69}{space 1} .5625884
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(income): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{err}Warning: 2% of the resampled realizations for _pm_1 are exactly identical to original value
{p2colset 7 17 21 2}{...}

{txt}{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  income
       {txt}Strata{res}:  7

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     43{col 35}    100{col 43} 0.4300{col 51} 0.0495{col 59}  .331391{col 69}{space 1} .5328663
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. logit no_shop treatment

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gender): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gender
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     40{col 35}    100{col 43} 0.4000{col 51} 0.0490{col 59} .3032948{col 69}{space 1} .5027908
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(shelter): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  shelter
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     50{col 35}    100{col 43} 0.5000{col 51} 0.0500{col 59} .3983211{col 69}{space 1} .6016789
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(job_loss): logit no_shop treatment
{err}variable {bf}job_loss{sf} not found
(error in option {bf:strata()})
{txt}{search r(111), local:r(111);}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(jobloss): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  jobloss
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     44{col 35}    100{col 43} 0.4400{col 51} 0.0496{col 59}  .340836{col 69}{space 1} .5428125
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gov_ideology): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gov_ideology
       {txt}Strata{res}:  10

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     41{col 35}    100{col 43} 0.4100{col 51} 0.0492{col 59}   .31262{col 69}{space 1} .5128558
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(white): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  white
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     40{col 35}    100{col 43} 0.4000{col 51} 0.0490{col 59} .3032948{col 69}{space 1} .5027908
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(gop): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  gop
       {txt}Strata{res}:  2

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     43{col 35}    100{col 43} 0.4300{col 51} 0.0495{col 59}  .331391{col 69}{space 1} .5328663
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. logit no_shop cdc_m pres_m state_m expert_m health_frame cdc_frame_h pres_frame_h state_frame_h expert_frame_h gender education white gop shelter jobloss ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.82287}  
Iteration 2:{space 3}log likelihood = {res:-732.14831}  
Iteration 3:{space 3}log likelihood = {res:-732.14766}  
Iteration 4:{space 3}log likelihood = {res:-732.14766}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}     74.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.14766{txt}{col 49}Pseudo R2{col 67}= {res}    0.0483

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       no_shop{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}cdc_m {c |}{col 16}{res}{space 2} .2182732{col 28}{space 2}  .267345{col 39}{space 1}    0.82{col 48}{space 3}0.414{col 56}{space 4}-.3057134{col 69}{space 3} .7422598
{txt}{space 8}pres_m {c |}{col 16}{res}{space 2}-.1855399{col 28}{space 2} .2499183{col 39}{space 1}   -0.74{col 48}{space 3}0.458{col 56}{space 4}-.6753708{col 69}{space 3}  .304291
{txt}{space 7}state_m {c |}{col 16}{res}{space 2} .0808928{col 28}{space 2} .2600986{col 39}{space 1}    0.31{col 48}{space 3}0.756{col 56}{space 4}-.4288911{col 69}{space 3} .5906768
{txt}{space 6}expert_m {c |}{col 16}{res}{space 2} .1690829{col 28}{space 2} .2600843{col 39}{space 1}    0.65{col 48}{space 3}0.516{col 56}{space 4} -.340673{col 69}{space 3} .6788387
{txt}{space 2}health_frame {c |}{col 16}{res}{space 2} .8441709{col 28}{space 2} .2998053{col 39}{space 1}    2.82{col 48}{space 3}0.005{col 56}{space 4} .2565633{col 69}{space 3} 1.431778
{txt}{space 3}cdc_frame_h {c |}{col 16}{res}{space 2}-.2408493{col 28}{space 2} .4332957{col 39}{space 1}   -0.56{col 48}{space 3}0.578{col 56}{space 4}-1.090093{col 69}{space 3} .6083946
{txt}{space 2}pres_frame_h {c |}{col 16}{res}{space 2} .1484537{col 28}{space 2} .4164738{col 39}{space 1}    0.36{col 48}{space 3}0.722{col 56}{space 4}  -.66782{col 69}{space 3} .9647275
{txt}{space 1}state_frame_h {c |}{col 16}{res}{space 2}-.3990044{col 28}{space 2} .4216742{col 39}{space 1}   -0.95{col 48}{space 3}0.344{col 56}{space 4}-1.225471{col 69}{space 3}  .427462
{txt}expert_frame_h {c |}{col 16}{res}{space 2}-.7915489{col 28}{space 2} .4071213{col 39}{space 1}   -1.94{col 48}{space 3}0.052{col 56}{space 4}-1.589492{col 69}{space 3} .0063942
{txt}{space 8}gender {c |}{col 16}{res}{space 2}-.6713646{col 28}{space 2} .1313187{col 39}{space 1}   -5.11{col 48}{space 3}0.000{col 56}{space 4}-.9287445{col 69}{space 3}-.4139847
{txt}{space 5}education {c |}{col 16}{res}{space 2}  .058688{col 28}{space 2} .0464818{col 39}{space 1}    1.26{col 48}{space 3}0.207{col 56}{space 4}-.0324147{col 69}{space 3} .1497907
{txt}{space 9}white {c |}{col 16}{res}{space 2} .4461157{col 28}{space 2} .1559373{col 39}{space 1}    2.86{col 48}{space 3}0.004{col 56}{space 4} .1404841{col 69}{space 3} .7517472
{txt}{space 11}gop {c |}{col 16}{res}{space 2}-.0521859{col 28}{space 2} .1417462{col 39}{space 1}   -0.37{col 48}{space 3}0.713{col 56}{space 4}-.3300034{col 69}{space 3} .2256316
{txt}{space 7}shelter {c |}{col 16}{res}{space 2} .0461846{col 28}{space 2} .1582116{col 39}{space 1}    0.29{col 48}{space 3}0.770{col 56}{space 4}-.2639045{col 69}{space 3} .3562737
{txt}{space 7}jobloss {c |}{col 16}{res}{space 2} .0332535{col 28}{space 2} .1400279{col 39}{space 1}    0.24{col 48}{space 3}0.812{col 56}{space 4}-.2411961{col 69}{space 3} .3077031
{txt}{space 3}ideology_rs {c |}{col 16}{res}{space 2} .0077646{col 28}{space 2} .0025937{col 39}{space 1}    2.99{col 48}{space 3}0.003{col 56}{space 4}  .002681{col 69}{space 3} .0128482
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.0031843{col 28}{space 2} .3265344{col 39}{space 1}   -0.01{col 48}{space 3}0.992{col 56}{space 4}-.6431799{col 69}{space 3} .6368114
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit no_shop i.cdc_m i.pres_m i.state_m i.expert_m i.health_frame ///cdc_frame_h pres_frame_h state_frame_h expert_frame_h gender education white gop shelter jobloss ideology_rs
{err}'/' not allowed in varlist
{txt}{search r(198), local:r(198);}

{com}. logit no_shop i.cdc_m i.pres_m i.state_m i.expert_m i.health_frame cdc_m##health_frame pres_m##health_frame state_m##health_frame expert_m##health_frame i.gender i.education i.white i.gop i.shelter i.jobloss ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.70205}  
Iteration 2:{space 3}log likelihood = {res:-732.02412}  
Iteration 3:{space 3}log likelihood = {res:-732.02346}  
Iteration 4:{space 3}log likelihood = {res:-732.02346}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     74.51
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.02346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0484

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              no_shop{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.cdc_m {c |}{col 23}{res}{space 2} .2215653{col 35}{space 2} .2674704{col 46}{space 1}    0.83{col 55}{space 3}0.407{col 63}{space 4}-.3026671{col 76}{space 3} .7457978
{txt}{space 13}1.pres_m {c |}{col 23}{res}{space 2} -.176816{col 35}{space 2}  .250565{col 46}{space 1}   -0.71{col 55}{space 3}0.480{col 63}{space 4}-.6679144{col 76}{space 3} .3142824
{txt}{space 12}1.state_m {c |}{col 23}{res}{space 2} .0902893{col 35}{space 2} .2609351{col 46}{space 1}    0.35{col 55}{space 3}0.729{col 63}{space 4} -.421134{col 76}{space 3} .6017126
{txt}{space 11}1.expert_m {c |}{col 23}{res}{space 2} .1738325{col 35}{space 2} .2606736{col 46}{space 1}    0.67{col 55}{space 3}0.505{col 63}{space 4}-.3370784{col 76}{space 3} .6847433
{txt}{space 7}1.health_frame {c |}{col 23}{res}{space 2} .8502493{col 35}{space 2} .3002697{col 46}{space 1}    2.83{col 55}{space 3}0.005{col 63}{space 4} .2617316{col 76}{space 3} 1.438767
{txt}{space 21} {c |}
{space 3}cdc_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.2444159{col 35}{space 2}  .433415{col 46}{space 1}   -0.56{col 55}{space 3}0.573{col 63}{space 4}-1.093894{col 76}{space 3}  .605062
{txt}{space 21} {c |}
{space 2}pres_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2} .1379651{col 35}{space 2} .4179334{col 46}{space 1}    0.33{col 55}{space 3}0.741{col 63}{space 4}-.6811693{col 76}{space 3} .9570996
{txt}{space 21} {c |}
{space 1}state_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.4023204{col 35}{space 2} .4225225{col 46}{space 1}   -0.95{col 55}{space 3}0.341{col 63}{space 4}-1.230449{col 76}{space 3} .4258086
{txt}{space 21} {c |}
expert_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.7981591{col 35}{space 2} .4076997{col 46}{space 1}   -1.96{col 55}{space 3}0.050{col 63}{space 4}-1.597236{col 76}{space 3} .0009175
{txt}{space 21} {c |}
{space 13}1.gender {c |}{col 23}{res}{space 2}-.6682574{col 35}{space 2} .1315977{col 46}{space 1}   -5.08{col 55}{space 3}0.000{col 63}{space 4}-.9261842{col 76}{space 3}-.4103305
{txt}{space 21} {c |}
{space 12}education {c |}
{space 19}2  {c |}{col 23}{res}{space 2} .1282037{col 35}{space 2} .1951309{col 46}{space 1}    0.66{col 55}{space 3}0.511{col 63}{space 4}-.2542458{col 76}{space 3} .5106533
{txt}{space 19}3  {c |}{col 23}{res}{space 2} .1528409{col 35}{space 2} .2420068{col 46}{space 1}    0.63{col 55}{space 3}0.528{col 63}{space 4}-.3214837{col 76}{space 3} .6271655
{txt}{space 19}4  {c |}{col 23}{res}{space 2} .1688025{col 35}{space 2} .1914823{col 46}{space 1}    0.88{col 55}{space 3}0.378{col 63}{space 4}-.2064959{col 76}{space 3} .5441009
{txt}{space 19}5  {c |}{col 23}{res}{space 2} .2877566{col 35}{space 2} .2152664{col 46}{space 1}    1.34{col 55}{space 3}0.181{col 63}{space 4}-.1341578{col 76}{space 3} .7096711
{txt}{space 21} {c |}
{space 14}1.white {c |}{col 23}{res}{space 2} .4471406{col 35}{space 2}  .156007{col 46}{space 1}    2.87{col 55}{space 3}0.004{col 63}{space 4} .1413725{col 76}{space 3} .7529087
{txt}{space 16}1.gop {c |}{col 23}{res}{space 2}-.0519727{col 35}{space 2} .1418125{col 46}{space 1}   -0.37{col 55}{space 3}0.714{col 63}{space 4}-.3299201{col 76}{space 3} .2259748
{txt}{space 12}1.shelter {c |}{col 23}{res}{space 2} .0471738{col 35}{space 2} .1583982{col 46}{space 1}    0.30{col 55}{space 3}0.766{col 63}{space 4} -.263281{col 76}{space 3} .3576285
{txt}{space 12}1.jobloss {c |}{col 23}{res}{space 2} .0316263{col 35}{space 2} .1401776{col 46}{space 1}    0.23{col 55}{space 3}0.822{col 63}{space 4}-.2431168{col 76}{space 3} .3063693
{txt}{space 10}ideology_rs {c |}{col 23}{res}{space 2} .0077289{col 35}{space 2} .0025962{col 46}{space 1}    2.98{col 55}{space 3}0.003{col 63}{space 4} .0026405{col 76}{space 3} .0128173
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0210837{col 35}{space 2} .3293268{col 46}{space 1}    0.06{col 55}{space 3}0.949{col 63}{space 4} -.624385{col 76}{space 3} .6665525
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} .7414562{col 26}{space 2} .0115993{col 37}{space 1}   63.92{col 46}{space 3}0.000{col 54}{space 4} .7187219{col 67}{space 3} .7641904
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: {p_end}
{res}
{com}. margins cdc_m#health_frame
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}     Margin{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
cdc_m#health_frame {c |}
{space 14}0 0  {c |}{col 20}{res}{space 2}  .684183{col 32}{space 2}  .019783{col 43}{space 1}   34.58{col 52}{space 3}0.000{col 60}{space 4}  .645409{col 73}{space 3}  .722957
{txt}{space 14}0 1  {c |}{col 20}{res}{space 2} .7986459{col 32}{space 2} .0182701{col 43}{space 1}   43.71{col 52}{space 3}0.000{col 60}{space 4} .7628371{col 73}{space 3} .8344547
{txt}{space 14}1 0  {c |}{col 20}{res}{space 2} .7282739{col 32}{space 2} .0447495{col 43}{space 1}   16.27{col 52}{space 3}0.000{col 60}{space 4} .6405665{col 73}{space 3} .8159813
{txt}{space 14}1 1  {c |}{col 20}{res}{space 2} .7950904{col 32}{space 2} .0470239{col 43}{space 1}   16.91{col 52}{space 3}0.000{col 60}{space 4} .7029253{col 73}{space 3} .8872555
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: cdc_m health_frame{p_end}
{res}
{com}. margins pres_m#health_frame
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33} Delta-method
{col 21}{c |}     Margin{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pres_m#health_frame {c |}
{space 15}0 0  {c |}{col 21}{res}{space 2} .7006731{col 33}{space 2} .0200989{col 44}{space 1}   34.86{col 53}{space 3}0.000{col 61}{space 4}   .66128{col 74}{space 3} .7400663
{txt}{space 15}0 1  {c |}{col 21}{res}{space 2} .7991892{col 33}{space 2}   .01863{col 44}{space 1}   42.90{col 53}{space 3}0.000{col 61}{space 4} .7626751{col 74}{space 3} .8357033
{txt}{space 15}1 0  {c |}{col 21}{res}{space 2} .6639278{col 33}{space 2} .0453763{col 44}{space 1}   14.63{col 53}{space 3}0.000{col 61}{space 4}  .574992{col 74}{space 3} .7528637
{txt}{space 15}1 1  {c |}{col 21}{res}{space 2} .7931305{col 33}{space 2} .0455305{col 44}{space 1}   17.42{col 53}{space 3}0.000{col 61}{space 4} .7038923{col 74}{space 3} .8823687
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. margins pres_m#health_frame
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33} Delta-method
{col 21}{c |}     Margin{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
pres_m#health_frame {c |}
{space 15}0 0  {c |}{col 21}{res}{space 2} .7006731{col 33}{space 2} .0200989{col 44}{space 1}   34.86{col 53}{space 3}0.000{col 61}{space 4}   .66128{col 74}{space 3} .7400663
{txt}{space 15}0 1  {c |}{col 21}{res}{space 2} .7991892{col 33}{space 2}   .01863{col 44}{space 1}   42.90{col 53}{space 3}0.000{col 61}{space 4} .7626751{col 74}{space 3} .8357033
{txt}{space 15}1 0  {c |}{col 21}{res}{space 2} .6639278{col 33}{space 2} .0453763{col 44}{space 1}   14.63{col 53}{space 3}0.000{col 61}{space 4}  .574992{col 74}{space 3} .7528637
{txt}{space 15}1 1  {c |}{col 21}{res}{space 2} .7931305{col 33}{space 2} .0455305{col 44}{space 1}   17.42{col 53}{space 3}0.000{col 61}{space 4} .7038923{col 74}{space 3} .8823687
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: pres_m health_frame{p_end}
{res}
{com}. {res}{com}graph export "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph.tif", as(tif) name("Graph")
{txt}(file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph.tif written in TIFF format)
{com}graph save "Graph" "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph.gph"
{res}{txt}(file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph.gph saved)
{com}margins expert_m#health_frame
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}     Margin{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
expert_m#health_frame {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .6854517{col 35}{space 2} .0201199{col 46}{space 1}   34.07{col 55}{space 3}0.000{col 63}{space 4} .6460174{col 76}{space 3} .7248859
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .8189814{col 35}{space 2}  .018021{col 46}{space 1}   45.45{col 55}{space 3}0.000{col 63}{space 4} .7836609{col 76}{space 3} .8543019
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .7202575{col 35}{space 2} .0435884{col 46}{space 1}   16.52{col 55}{space 3}0.000{col 63}{space 4} .6348258{col 76}{space 3} .8056893
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7122857{col 35}{space 2} .0516116{col 46}{space 1}   13.80{col 55}{space 3}0.000{col 63}{space 4} .6111289{col 76}{space 3} .8134425
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: expert_m health_frame{p_end}
{res}
{com}. {res}{com}graph save "Graph" "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph2.gph"
{res}{txt}(file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph2.gph saved)
{com}graph export "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph2.tif", as(tif) name("Graph")
{txt}(file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\Graph2.tif written in TIFF format)
{com}margins health_frame#expert_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}     Margin{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#expert_m {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .6854517{col 35}{space 2} .0201199{col 46}{space 1}   34.07{col 55}{space 3}0.000{col 63}{space 4} .6460174{col 76}{space 3} .7248859
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .7202575{col 35}{space 2} .0435884{col 46}{space 1}   16.52{col 55}{space 3}0.000{col 63}{space 4} .6348258{col 76}{space 3} .8056893
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .8189814{col 35}{space 2}  .018021{col 46}{space 1}   45.45{col 55}{space 3}0.000{col 63}{space 4} .7836609{col 76}{space 3} .8543019
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7122857{col 35}{space 2} .0516116{col 46}{space 1}   13.80{col 55}{space 3}0.000{col 63}{space 4} .6111289{col 76}{space 3} .8134425
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: health_frame expert_m{p_end}
{res}
{com}. margins health_frame#expert_m health_frame##pres_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}     Margin{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#expert_m {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .6854517{col 35}{space 2} .0201199{col 46}{space 1}   34.07{col 55}{space 3}0.000{col 63}{space 4} .6460174{col 76}{space 3} .7248859
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .7202575{col 35}{space 2} .0435884{col 46}{space 1}   16.52{col 55}{space 3}0.000{col 63}{space 4} .6348258{col 76}{space 3} .8056893
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .8189814{col 35}{space 2}  .018021{col 46}{space 1}   45.45{col 55}{space 3}0.000{col 63}{space 4} .7836609{col 76}{space 3} .8543019
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7122857{col 35}{space 2} .0516116{col 46}{space 1}   13.80{col 55}{space 3}0.000{col 63}{space 4} .6111289{col 76}{space 3} .8134425
{txt}{space 21} {c |}
{space 9}health_frame {c |}
{space 19}0  {c |}{col 23}{res}{space 2} .6926272{col 35}{space 2} .0168877{col 46}{space 1}   41.01{col 55}{space 3}0.000{col 63}{space 4} .6595279{col 76}{space 3} .7257266
{txt}{space 19}1  {c |}{col 23}{res}{space 2} .7980489{col 35}{space 2} .0158153{col 46}{space 1}   50.46{col 55}{space 3}0.000{col 63}{space 4} .7670514{col 76}{space 3} .8290463
{txt}{space 21} {c |}
{space 15}pres_m {c |}
{space 19}0  {c |}{col 23}{res}{space 2} .7462572{col 35}{space 2} .0137718{col 46}{space 1}   54.19{col 55}{space 3}0.000{col 63}{space 4} .7192651{col 76}{space 3} .7732494
{txt}{space 19}1  {c |}{col 23}{res}{space 2} .7237672{col 35}{space 2}  .032196{col 46}{space 1}   22.48{col 55}{space 3}0.000{col 63}{space 4} .6606643{col 76}{space 3} .7868702
{txt}{space 21} {c |}
{space 2}health_frame#pres_m {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .7006731{col 35}{space 2} .0200989{col 46}{space 1}   34.86{col 55}{space 3}0.000{col 63}{space 4}   .66128{col 76}{space 3} .7400663
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .6639278{col 35}{space 2} .0453763{col 46}{space 1}   14.63{col 55}{space 3}0.000{col 63}{space 4}  .574992{col 76}{space 3} .7528637
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .7991892{col 35}{space 2}   .01863{col 46}{space 1}   42.90{col 55}{space 3}0.000{col 63}{space 4} .7626751{col 76}{space 3} .8357033
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7931305{col 35}{space 2} .0455305{col 46}{space 1}   17.42{col 55}{space 3}0.000{col 63}{space 4} .7038923{col 76}{space 3} .8823687
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: health_frame expert_m pres_m{p_end}
{res}
{com}. margins health_frame#expert_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}     Margin{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#expert_m {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .6854517{col 35}{space 2} .0201199{col 46}{space 1}   34.07{col 55}{space 3}0.000{col 63}{space 4} .6460174{col 76}{space 3} .7248859
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .7202575{col 35}{space 2} .0435884{col 46}{space 1}   16.52{col 55}{space 3}0.000{col 63}{space 4} .6348258{col 76}{space 3} .8056893
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .8189814{col 35}{space 2}  .018021{col 46}{space 1}   45.45{col 55}{space 3}0.000{col 63}{space 4} .7836609{col 76}{space 3} .8543019
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7122857{col 35}{space 2} .0516116{col 46}{space 1}   13.80{col 55}{space 3}0.000{col 63}{space 4} .6111289{col 76}{space 3} .8134425
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot, recast(line) recastci(rarea)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: health_frame expert_m{p_end}
{res}
{com}. margins health_frame#pres_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33} Delta-method
{col 21}{c |}     Margin{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#pres_m {c |}
{space 15}0 0  {c |}{col 21}{res}{space 2} .7006731{col 33}{space 2} .0200989{col 44}{space 1}   34.86{col 53}{space 3}0.000{col 61}{space 4}   .66128{col 74}{space 3} .7400663
{txt}{space 15}0 1  {c |}{col 21}{res}{space 2} .6639278{col 33}{space 2} .0453763{col 44}{space 1}   14.63{col 53}{space 3}0.000{col 61}{space 4}  .574992{col 74}{space 3} .7528637
{txt}{space 15}1 0  {c |}{col 21}{res}{space 2} .7991892{col 33}{space 2}   .01863{col 44}{space 1}   42.90{col 53}{space 3}0.000{col 61}{space 4} .7626751{col 74}{space 3} .8357033
{txt}{space 15}1 1  {c |}{col 21}{res}{space 2} .7931305{col 33}{space 2} .0455305{col 44}{space 1}   17.42{col 53}{space 3}0.000{col 61}{space 4} .7038923{col 74}{space 3} .8823687
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. marginsplot, recast(line) recastci(rarea)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: health_frame pres_m{p_end}
{res}
{com}. margins, dydx(health_frame expert_m pres_m)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pres_m 1.expert_m 1.health_frame}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}1.pres_m {c |}{col 16}{res}{space 2}  -.02249{col 28}{space 2} .0374375{col 39}{space 1}   -0.60{col 48}{space 3}0.548{col 56}{space 4}-.0958663{col 69}{space 3} .0508862
{txt}{space 4}1.expert_m {c |}{col 16}{res}{space 2}-.0308695{col 28}{space 2} .0385576{col 39}{space 1}   -0.80{col 48}{space 3}0.423{col 56}{space 4} -.106441{col 69}{space 3}  .044702
{txt}1.health_frame {c |}{col 16}{res}{space 2} .1054217{col 28}{space 2} .0232193{col 39}{space 1}    4.54{col 48}{space 3}0.000{col 56}{space 4} .0599127{col 69}{space 3} .1509306
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}
{com}. margins, dydx(health_frame health_frame##pres_m)
{err}invalid dydx() option;
levels of interactions not allowed
{txt}{search r(198), local:r(198);}

{com}. ritest treatment (_b[treatment]/_se[treatment]), strata(education): logit no_shop treatment
{txt}(running {bf:logit} on estimation sample)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-768.92723}  
Iteration 2:{space 3}log likelihood = {res:-768.92718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}1{txt}){col 67}= {res}      0.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4028
{txt}Log likelihood = {res}-768.92718{txt}{col 49}Pseudo R2{col 67}= {res}    0.0005

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     no_shop{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}treatment {c |}{col 14}{res}{space 2}-.0182549{col 26}{space 2} .0218299{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0610407{col 67}{space 3} .0245309
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.154852{col 26}{space 2} .1369048{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4} .8865236{col 67}{space 3} 1.423181
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

Resampling replications ({res}100{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
..................................................    50
..................................................   100
{p2colset 7 17 21 2}{...}

{p2col :command:}logit no_shop treatment{p_end}
{p2colset 9 17 21 2}{...}
{p2col :_pm_1:}{res:_b[treatment]/_se[treatment]}{p_end}
  res. var(s):  treatment
   Resampling:  Permuting treatment
Clust. var(s){res}:  __000000
     {txt}Clusters{res}:  1346
{txt}Strata var(s){res}:  education
       {txt}Strata{res}:  5

{col 1}{text}{hline 13}{c TT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{col 1}{text}T           {col 14}{c |}     T(obs){col 27}      c{col 35}      n{col 43}  p=c/n{col 51}  SE(p){col 59}[95% Conf. Interval]
{res}{col 1}{text}{hline 13}{c +}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
{space 7}_pm_1 {c |}{col 14}{result}{space 2}-.8362345{col 27}     31{col 35}    100{col 43} 0.3100{col 51} 0.0462{col 59} .2212888{col 69}{space 1} .4103146
{col 1}{text}{hline 13}{c BT}{hline 12}{hline 8}{hline 8}{hline 8}{hline 8}{hline 10}{hline 10}
Note: Confidence interval is with respect to p=c/n.
Note: c = #{|T| >= |T(obs)|}
{res}
{com}. logit no_shop i.cdc_m i.pres_m i.state_m i.expert_m i.health_frame cdc_m##health_frame pres_m##health_frame state_m##health_frame expert_m##health_frame i.gender i.education i.white i.gop i.shelter i.jobloss ideology_rs

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-769.27709}  
Iteration 1:{space 3}log likelihood = {res:-732.70205}  
Iteration 2:{space 3}log likelihood = {res:-732.02412}  
Iteration 3:{space 3}log likelihood = {res:-732.02346}  
Iteration 4:{space 3}log likelihood = {res:-732.02346}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 49}LR chi2({res}19{txt}){col 67}= {res}     74.51
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-732.02346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0484

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              no_shop{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}1.cdc_m {c |}{col 23}{res}{space 2} .2215653{col 35}{space 2} .2674704{col 46}{space 1}    0.83{col 55}{space 3}0.407{col 63}{space 4}-.3026671{col 76}{space 3} .7457978
{txt}{space 13}1.pres_m {c |}{col 23}{res}{space 2} -.176816{col 35}{space 2}  .250565{col 46}{space 1}   -0.71{col 55}{space 3}0.480{col 63}{space 4}-.6679144{col 76}{space 3} .3142824
{txt}{space 12}1.state_m {c |}{col 23}{res}{space 2} .0902893{col 35}{space 2} .2609351{col 46}{space 1}    0.35{col 55}{space 3}0.729{col 63}{space 4} -.421134{col 76}{space 3} .6017126
{txt}{space 11}1.expert_m {c |}{col 23}{res}{space 2} .1738325{col 35}{space 2} .2606736{col 46}{space 1}    0.67{col 55}{space 3}0.505{col 63}{space 4}-.3370784{col 76}{space 3} .6847433
{txt}{space 7}1.health_frame {c |}{col 23}{res}{space 2} .8502493{col 35}{space 2} .3002697{col 46}{space 1}    2.83{col 55}{space 3}0.005{col 63}{space 4} .2617316{col 76}{space 3} 1.438767
{txt}{space 21} {c |}
{space 3}cdc_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.2444159{col 35}{space 2}  .433415{col 46}{space 1}   -0.56{col 55}{space 3}0.573{col 63}{space 4}-1.093894{col 76}{space 3}  .605062
{txt}{space 21} {c |}
{space 2}pres_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2} .1379651{col 35}{space 2} .4179334{col 46}{space 1}    0.33{col 55}{space 3}0.741{col 63}{space 4}-.6811693{col 76}{space 3} .9570996
{txt}{space 21} {c |}
{space 1}state_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.4023204{col 35}{space 2} .4225225{col 46}{space 1}   -0.95{col 55}{space 3}0.341{col 63}{space 4}-1.230449{col 76}{space 3} .4258086
{txt}{space 21} {c |}
expert_m#health_frame {c |}
{space 17}1 1  {c |}{col 23}{res}{space 2}-.7981591{col 35}{space 2} .4076997{col 46}{space 1}   -1.96{col 55}{space 3}0.050{col 63}{space 4}-1.597236{col 76}{space 3} .0009175
{txt}{space 21} {c |}
{space 13}1.gender {c |}{col 23}{res}{space 2}-.6682574{col 35}{space 2} .1315977{col 46}{space 1}   -5.08{col 55}{space 3}0.000{col 63}{space 4}-.9261842{col 76}{space 3}-.4103305
{txt}{space 21} {c |}
{space 12}education {c |}
{space 19}2  {c |}{col 23}{res}{space 2} .1282037{col 35}{space 2} .1951309{col 46}{space 1}    0.66{col 55}{space 3}0.511{col 63}{space 4}-.2542458{col 76}{space 3} .5106533
{txt}{space 19}3  {c |}{col 23}{res}{space 2} .1528409{col 35}{space 2} .2420068{col 46}{space 1}    0.63{col 55}{space 3}0.528{col 63}{space 4}-.3214837{col 76}{space 3} .6271655
{txt}{space 19}4  {c |}{col 23}{res}{space 2} .1688025{col 35}{space 2} .1914823{col 46}{space 1}    0.88{col 55}{space 3}0.378{col 63}{space 4}-.2064959{col 76}{space 3} .5441009
{txt}{space 19}5  {c |}{col 23}{res}{space 2} .2877566{col 35}{space 2} .2152664{col 46}{space 1}    1.34{col 55}{space 3}0.181{col 63}{space 4}-.1341578{col 76}{space 3} .7096711
{txt}{space 21} {c |}
{space 14}1.white {c |}{col 23}{res}{space 2} .4471406{col 35}{space 2}  .156007{col 46}{space 1}    2.87{col 55}{space 3}0.004{col 63}{space 4} .1413725{col 76}{space 3} .7529087
{txt}{space 16}1.gop {c |}{col 23}{res}{space 2}-.0519727{col 35}{space 2} .1418125{col 46}{space 1}   -0.37{col 55}{space 3}0.714{col 63}{space 4}-.3299201{col 76}{space 3} .2259748
{txt}{space 12}1.shelter {c |}{col 23}{res}{space 2} .0471738{col 35}{space 2} .1583982{col 46}{space 1}    0.30{col 55}{space 3}0.766{col 63}{space 4} -.263281{col 76}{space 3} .3576285
{txt}{space 12}1.jobloss {c |}{col 23}{res}{space 2} .0316263{col 35}{space 2} .1401776{col 46}{space 1}    0.23{col 55}{space 3}0.822{col 63}{space 4}-.2431168{col 76}{space 3} .3063693
{txt}{space 10}ideology_rs {c |}{col 23}{res}{space 2} .0077289{col 35}{space 2} .0025962{col 46}{space 1}    2.98{col 55}{space 3}0.003{col 63}{space 4} .0026405{col 76}{space 3} .0128173
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0210837{col 35}{space 2} .3293268{col 46}{space 1}    0.06{col 55}{space 3}0.949{col 63}{space 4} -.624385{col 76}{space 3} .6665525
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins health_frame#expert_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35} Delta-method
{col 23}{c |}     Margin{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#expert_m {c |}
{space 17}0 0  {c |}{col 23}{res}{space 2} .6854517{col 35}{space 2} .0201199{col 46}{space 1}   34.07{col 55}{space 3}0.000{col 63}{space 4} .6460174{col 76}{space 3} .7248859
{txt}{space 17}0 1  {c |}{col 23}{res}{space 2} .7202575{col 35}{space 2} .0435884{col 46}{space 1}   16.52{col 55}{space 3}0.000{col 63}{space 4} .6348258{col 76}{space 3} .8056893
{txt}{space 17}1 0  {c |}{col 23}{res}{space 2} .8189814{col 35}{space 2}  .018021{col 46}{space 1}   45.45{col 55}{space 3}0.000{col 63}{space 4} .7836609{col 76}{space 3} .8543019
{txt}{space 17}1 1  {c |}{col 23}{res}{space 2} .7122857{col 35}{space 2} .0516116{col 46}{space 1}   13.80{col 55}{space 3}0.000{col 63}{space 4} .6111289{col 76}{space 3} .8134425
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. margins health_frame#pres_m
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33} Delta-method
{col 21}{c |}     Margin{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
health_frame#pres_m {c |}
{space 15}0 0  {c |}{col 21}{res}{space 2} .7006731{col 33}{space 2} .0200989{col 44}{space 1}   34.86{col 53}{space 3}0.000{col 61}{space 4}   .66128{col 74}{space 3} .7400663
{txt}{space 15}0 1  {c |}{col 21}{res}{space 2} .6639278{col 33}{space 2} .0453763{col 44}{space 1}   14.63{col 53}{space 3}0.000{col 61}{space 4}  .574992{col 74}{space 3} .7528637
{txt}{space 15}1 0  {c |}{col 21}{res}{space 2} .7991892{col 33}{space 2}   .01863{col 44}{space 1}   42.90{col 53}{space 3}0.000{col 61}{space 4} .7626751{col 74}{space 3} .8357033
{txt}{space 15}1 1  {c |}{col 21}{res}{space 2} .7931305{col 33}{space 2} .0455305{col 44}{space 1}   17.42{col 53}{space 3}0.000{col 61}{space 4} .7038923{col 74}{space 3} .8823687
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. margins, dydx(health_frame expert_m pres_m)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,346
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(no_shop), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pres_m 1.expert_m 1.health_frame}{p_end}
{p2colreset}{...}

{res}{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28} Delta-method
{col 16}{c |}      dy/dx{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}1.pres_m {c |}{col 16}{res}{space 2}  -.02249{col 28}{space 2} .0374375{col 39}{space 1}   -0.60{col 48}{space 3}0.548{col 56}{space 4}-.0958663{col 69}{space 3} .0508862
{txt}{space 4}1.expert_m {c |}{col 16}{res}{space 2}-.0308695{col 28}{space 2} .0385576{col 39}{space 1}   -0.80{col 48}{space 3}0.423{col 56}{space 4} -.106441{col 69}{space 3}  .044702
{txt}1.health_frame {c |}{col 16}{res}{space 2} .1054217{col 28}{space 2} .0232193{col 39}{space 1}    4.54{col 48}{space 3}0.000{col 56}{space 4} .0599127{col 69}{space 3} .1509306
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 80}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}
{com}. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study14R.dta", replace
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study14R.dta saved

{com}. keep no_shop cdc_m pres_m state_m expert_m health_frame shelter jobloss cdc_frame_h pres_frame_h state_frame_h expert_frame_h white education gender gop ideology_rs

. save "C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1_replication.dta"
{txt}file C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\COVID_Study1_replication.dta saved

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\adeslatt\Documents\Local Government Sustainability\Administrative Behavior\MTurk Sustainability Studies\COVID-19\data\covid_processing.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Apr 2020, 13:39:12
{txt}{.-}
{smcl}
{txt}{sf}{ul off}